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Best GPU for Qwen Models in 2026 (Qwen 3 + 3.6 Picks)

Thurmon Demich — Sat, 18 Jul 2026 01:14:08 +0000

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

Qwen 2.5 7B runs well on an RTX 4060 Ti 16GB at $400. For Qwen 2.5 32B — the quality sweet spot of the lineup — you need an RTX 4090 24GB at minimum. Qwen 2.5 72B requires an RTX 5090 with heavy quantization or dual GPUs.

Qwen 2.5 full model lineup

Alibaba's Qwen 2.5 series covers an unusually wide range of sizes, from edge deployment at 0.5B to near-frontier performance at 72B:

Model	Parameters	FP16 Size	Q4_K_M Size	Minimum VRAM
Qwen 2.5 0.5B	0.5B	~1GB	~0.4GB	4GB
Qwen 2.5 1.5B	1.5B	~3GB	~1GB	4GB
Qwen 2.5 3B	3B	~6GB	~2GB	6GB
Qwen 2.5 7B	7B	~14GB	~4.5GB	8GB
Qwen 2.5 14B	14B	~28GB	~8.5GB	12GB
Qwen 2.5 32B	32B	~64GB	~19GB	24GB
Qwen 2.5 72B	72B	~144GB	~42GB	32GB+

The 0.5B to 3B models run on virtually any hardware including integrated graphics. The 7B and 14B models are the sweet spot for most local users. The 32B model is where Qwen really stands out — its reasoning quality rivals many 70B competitors while fitting on a single RTX 4090.

VRAM chart available at the original article

Qwen 2.5 coding model variants

Qwen 2.5 includes dedicated coding variants alongside the general models:

Variant	Sizes Available	Specialty
Qwen 2.5-Coder	0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B	Code generation, completion, review
Qwen 2.5-Math	1.5B, 7B, 72B	Mathematical reasoning
Qwen 2.5 (base)	0.5B–72B	General instruction following

Qwen 2.5-Coder 32B is particularly notable — it rivals GPT-4o on several coding benchmarks while fitting on a single RTX 4090. If coding is your primary use case, the Coder variant at 32B on a 4090 is one of the most compelling local setups available.

Best GPUs for Qwen 2.5 7B and 14B

These are the most popular sizes for local deployment. The 7B handles fast chat and general tasks; the 14B adds noticeably better reasoning without requiring a large VRAM upgrade.

GPU	VRAM	Qwen 7B Q4	Qwen 7B Q8	Qwen 14B Q4	Price
RTX 5090	32GB	~95 tok/s	~80 tok/s	~65 tok/s	~$2,000
RTX 4090	24GB	~65 tok/s	~55 tok/s	~45 tok/s	~$1,600
RTX 5080	16GB	~55 tok/s	~45 tok/s	~38 tok/s	~$1,000
RTX 4070 Ti Super	16GB	~40 tok/s	~32 tok/s	~28 tok/s	~$700
RTX 4060 Ti 16GB	16GB	~35 tok/s	~28 tok/s	~22 tok/s	~$400
RTX 3060 12GB (used)	12GB	~30 tok/s	~18 tok/s	Tight	~$250

Qwen 2.5 14B at Q4_K_M needs about 8.5GB for model weights plus context overhead. A 16GB card fits it with moderate context headroom. A 24GB card gives full breathing room for Qwen 2.5's native 32K+ context support.

Qwen 2.5 14B: optimal quantization by VRAM

VRAM	Recommended Quant	Notes
12GB	Q4_K_M	Fits at short-medium context; tight at 8K+
16GB	Q6_K	Good balance of quality and context headroom
24GB	Q8	Full quality, 32K context comfortable
32GB	FP16	Maximum quality, all context lengths

Qwen 2.5 14B at Q6_K on a 16GB card is one of the best value propositions in local LLM. The 14B model at Q6 consistently outperforms older 7B models at FP16 on reasoning tasks. For the full quantization-by-quantization VRAM math on the 14B specifically, see how much VRAM for Qwen 14B. For Qwen 3 specifically, see how much VRAM Qwen 3 needs across the full model lineup.

Best GPUs for Qwen 2.5 32B

The 32B model is the standout in the lineup. At Q4_K_M it needs ~19GB, landing squarely in RTX 4090 territory.

GPU	Quantization	VRAM Used	Fits?	Notes
RTX 5090 (32GB)	Q6_K	~24GB	Yes	Near-Q8 quality, 8K context comfortable
RTX 5090 (32GB)	Q4_K_M	~19GB	Yes	Comfortable fit, long context OK
RTX 4090 (24GB)	Q4_K_M	~19GB	Yes	Good fit with 4K–8K context
RTX 4090 (24GB)	Q6_K	~24GB	Tight	Short context only
RTX 5080 (16GB)	Q3_K_M	~14GB	Tight	Quality degraded, minimal context

The RTX 4090 at Q4_K_M is the best value entry point for Qwen 32B. The RTX 5090 lets you push to Q6_K for noticeably better output quality with full context support.

Qwen 2.5 72B: dual GPU or cloud

Like other 70B-class models, Qwen 72B needs ~42GB at Q4_K_M. A single RTX 5090 can handle Q2_K (~26GB) or a very tight Q3_K_M (~33GB), but quality suffers below Q4. For quality Qwen 72B locally:

2x RTX 4090 (48GB) — fits Q4_K_M cleanly via tensor splitting
RTX 5090 + CPU offload — possible but slow; inference drops significantly
Cloud inference — Vast.ai or RunPod for occasional use without buying hardware

Qwen 2.5 vs Llama 3 vs Mistral: which is best for what?

Use Case	Best Model	Why
General chat	Llama 3 8B or Qwen 7B	Similar quality, Qwen slightly stronger multilingual
Coding	Qwen 2.5-Coder 14B or 32B	Dedicated coding training; beats Llama 3 at code
Mathematics	Qwen 2.5-Math or Qwen 32B	Purpose-built math training
Multilingual	Qwen 2.5 (any size)	Best non-English support, especially Chinese/Japanese/Korean
Reasoning at 32B	Qwen 2.5 32B	Beats most 70B competitors at this size
Fast responses	Mistral 7B	Extremely efficient for its quality level

Qwen 2.5 32B is competitive with Llama 3 70B on most English reasoning benchmarks while using roughly half the VRAM. If you need multilingual capability or coding, Qwen is the clear winner at its respective size.

Tok/s benchmarks: Qwen 2.5 vs comparable models

At Q4_K_M on an RTX 4060 Ti 16GB:

Model	Size	Tok/s	Notes
Mistral 7B	7B	~35 tok/s	Fastest 7B-class
Qwen 2.5 7B	7B	~33 tok/s	Slightly larger vocab overhead
Llama 3 8B	8B	~32 tok/s	Larger vocab than Llama 2
Qwen 2.5 14B	14B	~22 tok/s	Fits 16GB at Q4_K_M
Llama 2 13B	13B	~20 tok/s	Older architecture

The larger vocabulary in Qwen 2.5 adds a small overhead compared to Mistral 7B, but the quality difference for most tasks — especially multilingual and coding — more than compensates.

Which GPU should you buy for Qwen?

Running Qwen 2.5 7B for chat and general tasks? → RTX 4060 Ti 16GB ($400). Runs Q8 quantization comfortably with 8K context. Best budget entry point.

Running Qwen 2.5 14B as your daily driver? → RTX 4060 Ti 16GB ($400) minimum, RTX 4070 Ti Super ($700) preferred. 16GB fits Q6_K; the extra VRAM on 700-class cards helps with context headroom.

Running Qwen 2.5-Coder 32B (the best local coding setup)? → RTX 4090 ($1,600). Fits Q4_K_M (~19GB) comfortably with room for 4K–8K coding context.

Running Qwen 2.5 32B for quality reasoning? → RTX 4090 ($1,600). Same reasoning as above. Q4_K_M quality rivals many 70B models at half the VRAM.

Running Qwen 2.5 72B locally? → RTX 5090 ($2,000) for Q3_K_M single-card, 2x RTX 4090 ($3,200) for Q4_K_M quality.

Common mistakes to avoid

Overlooking Qwen 32B in favor of 72B — Qwen 2.5 32B rivals many 70B models in reasoning quality while fitting on a single RTX 4090. It is one of the best intelligence-per-dollar options available locally.
Buying 8GB VRAM for Qwen 7B — Qwen 7B fits at Q4 in 8GB, but you cannot run the excellent 14B variant at all. A 16GB card opens up both models.
Ignoring Qwen's long context capability — Qwen 2.5 supports 32K+ context natively. This capability requires significant KV cache VRAM; a 16GB card will run out at 32K on even the 7B model.
Not checking the Coder variant — Qwen 2.5-Coder models are trained specifically for code generation. If coding is your primary use case, the Coder variant outperforms the base model at equivalent sizes.

Our recommendation

Your goal	Best GPU	Price
Qwen 7B daily use	RTX 4060 Ti 16GB	~$400
Qwen 14B comfortable	RTX 4070 Ti Super	~$700
Qwen 32B (best value)	RTX 4090	~$1,600
Qwen 32B (best quality)	RTX 5090	~$2,000
Qwen 72B	2x RTX 4090	~$3,200

Qwen 2.5 32B on an RTX 4090 is one of the best price-to-intelligence ratios in local LLM right now. If your budget allows, start there.

If you run multiple Qwen variants through Ollama, keep in mind that Ollama loads one model at a time by default, so your VRAM only needs to fit the largest model you plan to run. For Qwen 3 itself, see our best GPU for Qwen 3 guide, and for the latest release in the family our best GPU for Qwen 3.6 guide. For VRAM planning across all models, the VRAM requirements guide covers every size systematically.

Related guides on Best GPU for LLM

Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.

Best GPU for Krea 2 in 2026: RAW vs Turbo (5 Picks)

Thurmon Demich — Fri, 17 Jul 2026 01:14:26 +0000

This article was originally published on Best GPU for AI. The full version with interactive tools, FAQ, and live pricing is on the original site.

Quick answer: The RTX 4070 Ti Super 16GB is the best GPU for Krea 2 for most people running the Turbo checkpoint — 8-step generation lands around 3-4 seconds at 1024px in FP8. If you plan to train LoRAs against Krea 2 RAW (the 12.9B undistilled model), skip 16GB entirely and go RTX 4090 24GB.

Who this is for

This guide is for image-generation enthusiasts and LoRA trainers who watched Krea AI open-source their in-house foundation model on 2026-06-22 and want to run it locally instead of paying for API credits. Krea 2 shipped in two flavors — a 12.9B RAW checkpoint (undistilled, LoRA-training-ready) and an 8-step distilled Turbo variant — and each has a very different hardware profile. It also ranked #1 text-to-image on the independent Artificial Analysis leaderboard at launch, which means the demand curve is real and the "cheapest card that runs it well" question matters more than usual.

If you're a Flux user asking whether your current rig carries over, mostly yes for Turbo, mostly no for RAW+LoRA. My best GPU for Flux.2 guide gives you the neighboring numbers if you want to run both models on one card. Qwen users, similarly, get the best GPU for Qwen Image breakdown.

RAW vs Turbo: the actual VRAM story

Krea 2 RAW is a 12.9B DiT diffusion transformer. In FP16 the weights alone sit around 26GB before you add the text encoder, VAE, and activation memory — meaning single-consumer-GPU FP16 is only comfortable on the RTX 5090. FP8 drops the base to roughly 14GB, which brings 16GB cards into play for inference. LoRA training against RAW is the interesting case: you need optimizer state plus gradients on the LoRA layers, which pushes practical VRAM to ~22-24GB even with 8-bit AdamW.

Krea 2 Turbo is the distilled variant tuned for 8-step inference. Same architecture, same tokenizer, but the sampling schedule collapses to a fraction of the compute. On a flagship it hits the ~2-second mark that VentureBeat quoted in the Krea release coverage. In FP8, Turbo fits inside 12GB, which is genuinely new territory for a top-ranked foundation image model.

Workload	FP16 VRAM	FP8 VRAM	Q4 (GGUF-style)
Krea 2 Turbo inference (1024px, 8 steps)	~26GB	~11-12GB	~8GB
Krea 2 Turbo + 1 ControlNet	~28GB	~14GB	~10GB
Krea 2 RAW inference (1024px, 28-40 steps)	~26GB	~14GB	~10GB
Krea 2 RAW + LoRA training (batch 1)	~34GB+	~22-24GB	not recommended
Krea 2 RAW + full ControlNet stack	~32GB	~18-20GB	~14GB

VRAM chart available at the original article

For a broader mental model of how VRAM budgets map to diffusion workloads generally, my how much VRAM for Stable Diffusion explainer walks through the base + text-encoder + activation math that produces these numbers.

GPU ranking for Krea 2 in 2026

Approximate ComfyUI times with the Diffusers Krea 2 pipeline (which landed July 2026), Euler-A sampler, no ControlNet, 1024×1024 output. Turbo is 8 steps, RAW is 28 steps.

GPU	VRAM	Turbo (8 step)	RAW (28 step)	Price
RTX 5090	32GB	~1.8s	~6s	~$2,000
RTX 4090	24GB	~2.2s	~8s	~$1,600
RTX 4070 Ti Super	16GB	~3.5s	~13s (FP8 only)	~$700
RTX 5070 Ti	16GB	~3.1s	~11s (FP8 only)	~$750
RTX 3090 (used)	24GB	~4.0s	~14s	~$700 used
RTX 4060 Ti 16GB	16GB	~7s	~24s (FP8 only)	~$400

The three columns you actually care about are VRAM, Turbo speed, and whether the card can touch RAW at all. Anything below 16GB is Turbo-only territory. Anything below 24GB is inference-only for RAW — no LoRA training.

Which GPU should YOU buy for Krea 2?

You'll only run Krea 2 Turbo, no LoRA training: RTX 4070 Ti Super at ~$700. 16GB is comfortable in FP8, and 3-4 second gens are inside the "I can prompt-iterate without losing focus" window.
You want faster Turbo and occasional ControlNet stacking: RTX 5070 Ti at ~$750. Same 16GB but Blackwell tensor cores knock about 15% off gen time versus Ada.
You'll run Krea 2 RAW inference (not training): RTX 3090 24GB used at ~$700 is genuinely the value pick. FP16 RAW fits with headroom, LoRA loading works, and used flagship VRAM beats new mid-range VRAM every time for this workload. The best GPU for ComfyUI guide covers the node graph you'll want.
You'll train LoRAs against Krea 2 RAW: RTX 4090 24GB is the floor. Optimizer state alone eats ~4-6GB on top of the FP8 base, and 16GB cards OOM the moment you unfreeze more than the smallest LoRA rank. My best GPU for LoRA training guide has the batch-size and gradient-accumulation math if you want to push rank higher.
You're a working artist who needs the fastest possible iteration: RTX 5090 at ~$2,000. Sub-2-second Turbo means the render never becomes the bottleneck — you become the bottleneck. This is the "money is worth less than time" pick.
You're on a budget and want to try it: RTX 4060 Ti 16GB at ~$400 runs Turbo in FP8. 7 seconds per image is slow for real-time prompt work but perfectly fine for batch generation.

The contrarian take: skip RAW entirely if you're not training LoRAs

Here's the thing most launch-week guides won't tell you: if you're not planning to train custom LoRAs, the RAW checkpoint is mostly a research artifact for you. Turbo output quality is within a few percentage points of RAW on the Artificial Analysis leaderboard, ships at ~4x the speed, and fits in half the VRAM. The reason RAW exists as a public download is that Krea open-sourced the undistilled weights so the community can build on them — LoRAs, DreamBooth, fine-tunes. If your workflow is prompt-in, image-out, then RAW's larger VRAM footprint is buying you nothing you'll perceive in the output.

This changes the buying calculus. A 16GB card is the right pick for probably 80% of Krea 2 users. The temptation is to spec for RAW "just in case," but if you're honest about your workflow — do you actually train LoRAs, or do you tell yourself you might? — the 16GB tier delivers the model at Turbo quality with real-time iteration, and the $700-900 you save funds a used display, an SSD upgrade, or another year of RunPod credits for the occasional RAW experiment.

Common mistakes with Krea 2

Buying an 8GB card for Turbo. Turbo runs in ~11-12GB FP8 with headroom, not 8. RTX 4060 8GB, RTX 3070, and similar will OOM the moment you add the text encoder or bump resolution to 1152px. 12GB is the floor and 16GB is where it stops being anxious.
Assuming the "12GB is fine for Flux" tier maps to Krea 2. Flux.1 Dev at 12B and Krea 2 at 12.9B look similar on paper, but Krea 2's text encoder and VAE are chunkier — the practical VRAM budget is 15-20% higher. Cards that scraped by on Flux.1 (RTX 4070 12GB, RTX 3060 12GB) will need Q4 quantization for Krea 2 Turbo and will still feel tight.
Trying to train Krea 2 RAW LoRAs on a 16GB card at FP16. Optimizer state plus gradients plus activations pushes total VRAM past 30GB even at LoRA rank 16. You will OOM. Options: drop to 8-bit AdamW + FP8 base (still ~22GB, so 24GB is the real floor), or move training to cloud. Do not buy a 16GB card expecting to train against RAW — this is the most expensive misread in the guide.
Ignoring the diffusers pipeline entirely and hand-porting from the Krea reference code. The July 2026 Diffusers integration handles FP8 casting, sampling scheduler, and text encoder pairing correctly. Every "why does my Krea 2 output look worse than the leaderboard samples" thread I've seen traces back to a custom loader that skipped one of these steps.

Final verdict

Budget	GPU	Krea 2 capability
$2,000	RTX 5090 32GB	RAW FP16, RAW LoRA training rank 128, Turbo sub-2s
$1,600	RTX 4090 24GB	RAW FP16 comfortable, LoRA training rank 32-64
$700	RTX 4070 Ti Super 16GB	Turbo in FP8 with ControlNet, RAW inference in FP8
$700 used	RTX 3090 24GB	RAW FP16 inference, LoRA training at low rank
$400	RTX 4060 Ti 16GB	Turbo in FP8 (slow), RAW inference in FP8 (tight)

If you're running Krea 2 Turbo, buy the RTX 4070 Ti Super 16GB; if you're training LoRAs against RAW, buy the RTX 4090 24GB — everything else is a compromise on one axis or the other.

Related guides on Best GPU for AI

The full version lives on Best GPU for AI — VRAM calculator, GPU comparison table, and live Amazon pricing.

Best GPU for Llama 3 in 2026 (8B-70B Picks Ranked)

Thurmon Demich — Thu, 16 Jul 2026 01:15:03 +0000

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

For Llama 3 8B, the RTX 4060 Ti 16GB at $400 handles it easily at Q8 quantization. For Llama 3 70B, you need the RTX 5090 32GB at minimum — or dual RTX 4090s for better quality. The 405B variant is cloud-only territory.

Llama 3 model family overview

Meta's Llama 3 lineup spans three sizes with very different hardware demands:

Model	Parameters	FP16 Size	Q4_K_M Size	Q8 Size	Minimum VRAM
Llama 3 8B	8B	~16GB	~4.5GB	~8.5GB	8GB (tight)
Llama 3 70B	70B	~140GB	~40GB	~75GB	32GB (Q2 only)
Llama 3 405B	405B	~810GB	~230GB	~430GB	Multi-GPU only

The 8B model is the everyday workhorse — fast, capable, and fits on nearly any modern GPU. The 70B model delivers substantially better reasoning and instruction-following but demands serious hardware. The 405B is primarily a research and enterprise tool that is out of reach for single-GPU setups.

VRAM chart available at the original article

Llama 3 vs Llama 2: hardware differences

Llama 3 introduces Grouped Query Attention (GQA) and a larger vocabulary (128K tokens vs 32K). Both changes have hardware implications:

GQA reduces KV cache size — Llama 3 8B uses less VRAM for its attention cache than Llama 2 13B, despite similar inference quality
Larger vocabulary adds a small VRAM overhead (~0.5GB at FP16) that most calculators undercount
Context length — Llama 3 supports 8K context by default vs 4K for Llama 2; longer context fills the KV cache faster

Net result: Llama 3 8B is roughly as demanding as Llama 2 7B on VRAM but more capable on output quality. Llama 3 70B is more demanding than Llama 2 70B due to its 8K context default.

Best GPUs for Llama 3 8B

The 8B model is lightweight enough to run on almost any modern GPU at Q4 quantization. The question is how fast you want it and what quantization quality you want.

GPU	VRAM	Llama 3 8B Q4_K_M	Llama 3 8B Q8	Price
RTX 5090	32GB	~95 tok/s	~85 tok/s	~$2,000
RTX 4090	24GB	~65 tok/s	~60 tok/s	~$1,600
RTX 5080	16GB	~55 tok/s	~50 tok/s	~$1,000
RTX 5070 Ti	16GB	~45 tok/s	~40 tok/s	~$750
RTX 4070 Ti Super	16GB	~40 tok/s	~35 tok/s	~$700
RTX 4060 Ti 16GB	16GB	~35 tok/s	~28 tok/s	~$400
RTX 3060 12GB (used)	12GB	~30 tok/s	~18 tok/s	~$250

At Q4_K_M, the 8B model uses about 4.5GB of VRAM plus context overhead. Even the RTX 3060 12GB handles it comfortably with room for 8K+ context. The RTX 4060 Ti 16GB lets you run Q8 — noticeably better output quality with only a modest speed drop. For an exact breakdown of Llama 3 8B VRAM needs across every quantization level, see how much VRAM for Llama 3 8B.

Optimal quantization per GPU tier for Llama 3 8B

GPU Tier	Recommended Quant	Why
8GB VRAM	Q4_K_M	Fits model + moderate context; Q8 is too tight
12GB VRAM	Q6_K or Q8	12GB gives Q6_K with long context or Q8 with short context
16GB VRAM	Q8	Comfortable fit with 8K context headroom
24GB+ VRAM	FP16	Full precision, maximum output quality

Q4_K_M is the minimum for good output quality. Q8 is the sweet spot for quality without using FP16's full VRAM cost.

Best GPUs for Llama 3 70B

This is where GPU selection matters most. At Q4_K_M, the 70B model requires roughly 40GB, which exceeds every single consumer GPU.

Setup	Quantization	VRAM Used	Fits?	Speed
RTX 5090 (32GB)	Q2_K	~25GB	Yes	~22 tok/s
RTX 5090 (32GB)	Q3_K_M	~32GB	Tight	~18 tok/s
RTX 5090 (32GB)	Q4_K_M	~40GB	No	—
2x RTX 4090 (48GB)	Q4_K_M	~40GB	Yes	~15 tok/s
2x RTX 4090 (48GB)	Q5_K_M	~46GB	Yes	~12 tok/s
RTX 4090 + CPU offload	Q4_K_M	Partial	Slow	~4 tok/s

For serious 70B use, dual RTX 4090s running via llama.cpp tensor splitting give you 48GB of fast VRAM and solid throughput. The RTX 5090 handles Q2_K or Q3_K_M on a single card, but quality degrades noticeably below Q4. The Q3_K_M fit is borderline — short context only.

For a deeper look at VRAM planning, see our VRAM requirements guide.

Ollama setup tips for Llama 3

Getting Llama 3 running well with Ollama takes a few minutes:

# Pull and run Llama 3 8B
ollama run llama3

# Pull a specific quantization (Q8 for 16GB cards)
ollama pull llama3:8b-instruct-q8_0

# For 70B on dual GPUs, set tensor split
CUDA_VISIBLE_DEVICES=0,1 ollama run llama3:70b

Ollama automatically selects Q4_K_M by default for the base llama3 tag. If you have 16GB VRAM, pulling the Q8 variant gives measurably better output quality for only ~30% more VRAM usage.

For dual-GPU setups, Ollama handles tensor splitting automatically when both GPUs are visible. You do not need to configure anything manually — it detects the available VRAM and distributes layers accordingly.

Llama 3 405B: not a single-GPU model

The 405B model needs 230GB+ even at Q4, making it multi-node or cloud-only. If you need 405B capability, look at cloud GPU providers or build a dedicated inference cluster. For most users, the 70B model provides excellent quality at a fraction of the hardware cost.

Which GPU should you buy for Llama 3?

Running Llama 3 8B for daily chat and coding? → RTX 4060 Ti 16GB ($400). Runs Q8 quantization with room for 8K context. Best price for the most common Llama 3 use case.

Running Llama 3 8B at maximum speed? → RTX 4090 ($1,600). Hits ~65 tok/s at Q4_K_M, FP16 fits comfortably. The 5090 adds ~30% more speed for $400 more.

Running Llama 3 70B on a single card? → RTX 5090 ($2,000). The only consumer GPU that fits 70B at any usable quantization. Q3_K_M on 32GB, borderline Q2_K.

Running Llama 3 70B at full Q4 quality? → 2x RTX 4090 ($3,200). 48GB combined VRAM, Q4_K_M fits cleanly with context headroom. The power-user choice for 70B.

Need 405B capability? → RunPod cloud. No consumer GPU handles this. A100 80GB instances on RunPod can run 405B at Q4 without the multi-GPU setup complexity.

Llama 3 8B vs 70B: which size should you run?

The 8B model is not just a stepping stone to 70B. For many tasks, 8B at Q8 on a 16GB card outperforms 70B at Q2 on a 32GB card — because quantization quality matters. If you are choosing between Llama 3 8B and Mistral 7B for your workload, our best GPU for Mistral guide offers a side-by-side perspective on where Mistral's architecture behaves differently under the same hardware.

Choose 8B when:

You need fast responses (35+ tok/s vs 15-22 tok/s)
Your tasks are chat, Q&A, summarization, or simple coding
Budget is a priority

Choose 70B when:

You need better reasoning and multi-step problem solving
Code quality at the complex-task level matters
You have the hardware to run it at Q4 or better

The 70B quality advantage is real but only shows clearly on hard benchmarks. For everyday chat, the 8B model is often "good enough" that users cannot tell the difference.

Common mistakes to avoid

Buying 8GB VRAM for Llama 3 8B — the model fits at Q4, but you will have almost no context headroom. At 4K+ context, the KV cache pushes you past 8GB, causing slowdowns or crashes.
Expecting to run 70B at good quality on a single consumer GPU — even the RTX 5090 limits you to Q3_K_M, where reasoning quality degrades. Plan for dual GPUs or accept the quality compromise.
Ignoring bandwidth when comparing GPUs — the RTX 3060 12GB (360 GB/s) produces faster inference than the RTX 4060 8GB (272 GB/s) for the same 8B model. Bandwidth per dollar matters for inference.
Not accounting for KV cache VRAM — Llama 3's default 8K context adds 2–4GB of KV cache on top of model weights. Factor this in before assuming a model fits.

Our recommendation

Your goal	Best GPU	Price
Llama 3 8B daily driver	RTX 4060 Ti 16GB	~$400
Llama 3 8B maximum speed	RTX 4090	~$1,600
Llama 3 70B (single GPU, Q3)	RTX 5090	~$2,000
Llama 3 70B (Q4 quality)	2x RTX 4090	~$3,200
Llama 3 405B	RunPod cloud	Pay per hour

If you are running models through Ollama, the same GPU picks apply — Ollama uses llama.cpp under the hood with automatic quantization selection. Want to fine-tune Llama 3 on your own data? The LLM fine-tuning GPU guide covers the additional VRAM overhead LoRA and full fine-tuning require. For the broader local LLM landscape, see our budget GPU guide and VRAM requirements.

Related guides on Best GPU for LLM

Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.

GPU Rental for AI: What to Rent and What It Costs (2026)

Thurmon Demich — Wed, 15 Jul 2026 01:15:11 +0000

Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Need an H100 for a weekend fine-tuning run but don't have $30,000 lying around? That is exactly the problem GPU rental solves — embarrassingly well, if you know which tier to rent and what a fair hourly rate looks like.

The catch is that almost everything ranking for "gpu rental" is written by the companies renting you the GPUs. Nobody tells you that an RTX 4090 at $0.35/hr handles most hobbyist workloads, or that paying H100 rates to run Stable Diffusion is like renting a semi truck for a grocery run. So here is the neutral version: what each tier costs as of mid-2026, and the traps that quietly drain your credit balance.

If your workload is bursty — a fine-tune this weekend, nothing for three weeks — renting is almost always the right call. Start with a marketplace like Vast.ai or a managed platform like RunPod and you can be running inside ten minutes.

How GPU rental actually works

There are two flavors of GPU rental, and the difference matters more than any pricing table.

Marketplaces (Vast.ai is the big one) connect you with thousands of individual hosts — everything from professional datacenters to someone's mining rig in a garage. Prices are set by supply and demand, which is why you can find an RTX 3060 for pocket change. Reliability varies by host, so check ratings before you commit.

Managed clouds (RunPod, Lambda, and similar) run their own datacenters or vetted partner facilities. You pay a modest premium for predictable uptime, network storage, and one-click templates. Our RunPod vs Vast.ai comparison covers the head-to-head if you want a specific platform.

The mechanics are the same either way: you pick a GPU, launch a container (most platforms offer one-click templates for PyTorch, ComfyUI, and Ollama), and get billed per second or per minute of runtime. No contracts. Stop the instance and the meter stops — mostly. More on that "mostly" below.

What GPU rental costs in mid-2026

Prices move weekly, so treat these as ballparks rather than quotes. But as of mid-2026, this is roughly what each tier costs on GPU-first providers:

Your workload	Rent this	Typical on-demand price
Stable Diffusion 1.5, small LLM inference	RTX 3060 12GB	from ~$0.02/hr (Vast.ai marketplace)
SDXL, Flux, 7B-13B LLMs, LoRA training	RTX 4090 24GB	from ~$0.35/hr
Heavier inference, video generation	RTX 5000-class	~$0.39/hr
Fine-tuning 13B-70B, serious training	A100 80GB	~$0.75-1.50/hr
Large fine-tunes, fast multi-GPU training	H100 80GB	~$2.00-3.00/hr
Long-context work, big batch inference	H200 141GB	~$2.60+/hr
Frontier-scale training	B200	~$3.50/hr where available

Two pricing levers can cut these numbers dramatically. Spot (interruptible) instances run 50-80% below on-demand rates — the catch is the provider can reclaim the machine with little warning, so they suit checkpointed training jobs and throwaway experiments. And avoiding hyperscalers matters more than any coupon code: AWS and GCP charge roughly 2-3x what GPU-first providers do for the same silicon — you are paying for enterprise compliance you probably don't need.

The pattern worth internalizing: rent the cheapest tier that fits your model in VRAM. An A100 at $1/hr fine-tunes a 13B model no better than a 4090 at a third of the price. (We have watched people burn $40 of H100 time on a job a 4090 finishes overnight for $3.)

The hidden costs nobody advertises

The hourly GPU rate is the headline. The bill is written in the fine print.

Storage bills separately, typically per GB per month, and keeps accruing while your instance is stopped. A 200GB checkpoint volume costs money every day whether you touch it or not.

Data egress — downloading your trained model or generated outputs — runs $0.05-0.12/GB on many providers. Pull a 50GB checkpoint set down twice and you have spent more than the training run.

Idle instances are the silent killer. The GPU bills whether it is computing or sitting at a bash prompt while you eat dinner. Set auto-stop timers, and actually terminate (not just stop) finished instances.

Can you rent to own a GPU?

Short answer: no. "GPU rent to own" gets searched a lot, but no mainstream provider offers a rental contract that ends in you owning the card. What exists instead is the uncomfortable math that renting long-term costs more than buying — an RTX 4090 at $0.35/hr crosses its ~$1,600 retail price after roughly 4,600 rental hours, and heavier daily use gets there within a year. Run your own numbers in our cloud vs local TCO calculator.

But that math cuts the other way too. If you genuinely use a GPU several hours every day, buying beats renting — a 24GB card you own covers the same workloads with no meter running, and holds resale value.
See the recommended pick on the original guide
That is the closest thing to "rent to own" that actually exists: rent to learn what you need, then buy it.

Common mistakes to avoid

Renting more GPU than the model needs. Match VRAM to workload first, prestige second. If it fits in 24GB, the 4090 tier is your answer.

Ignoring spot pricing for training. If your job checkpoints every few hundred steps, interruptible instances at half price (or less) are free money.

Leaving instances running overnight. The most common way beginners torch a $50 credit balance. Auto-stop exists — use it.

Uploading datasets over a slow pipe. A 100GB dataset over home upload speeds can take longer than the training job. Stage data in cloud storage near the provider instead.

Which route should you take?

The decision compresses to hours of use, not enthusiasm.

Rent if your GPU needs come in bursts — occasional fine-tunes, trying a 70B model before committing to hardware, or anything needing 80GB+ of VRAM no consumer card offers.

Buy if you run AI workloads daily and they fit in 24GB or less. At that usage the ownership math wins within months; our cloud vs home GPU breakdown walks through the break-even points, and the best GPU for AI guide covers what to buy at every budget.

Do both if you own a mid-range card for daily work and rent big iron for the occasional heavy job. This hybrid is where most serious hobbyists land.

Our verdict

For anyone whose GPU demand is spiky, rental is the best deal in AI hardware: 4090s from ~$0.35/hr, A100s from under a dollar, and per-second billing that ends the moment you stop. Start on a marketplace for the lowest prices, graduate to a managed platform when downtime starts costing you more than the premium. And run the TCO math the moment your usage becomes daily.

Rent the smallest GPU that fits your model, kill the instance the second you're done, and buy hardware only when the rental receipts tell you to.

GPU rental FAQ

How do you rent GPU power for AI?

Sign up with a GPU cloud provider like RunPod or Vast.ai, add credit, and launch an instance from a template (PyTorch, ComfyUI, Ollama, and similar are usually one click). You connect via browser or SSH and get billed per second or per minute of runtime. Most people go from signup to a running GPU in roughly 10-15 minutes.

How much does GPU rental cost?

As of mid-2026, budget cards like the RTX 3060 start at just a few cents per hour on marketplace platforms, an RTX 4090 typically runs in the $0.35-0.70 range, A100 80GB instances land around $0.75-1.50 per hour, and H100s cost roughly $2-3 per hour. Spot pricing can cut those rates by half or more.

Is renting a GPU worth it?

For bursty or occasional workloads, yes — renting avoids a four-figure upfront purchase and gives you access to datacenter GPUs no consumer can buy. For daily heavy use, ownership usually wins within several months to a year, because rental fees keep accruing while a purchased card is a one-time cost with resale value.

Can you rent to own a GPU?

No mainstream provider offers a true rent-to-own program for GPUs. Rental payments never build toward ownership, so long-term renters end up paying more than the card's retail price. If you expect sustained daily use, the practical move is renting briefly to confirm your requirements, then buying the GPU outright.

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Continue on Best GPU for AI for the complete guide with interactive calculators and current GPU prices.

Best GPU for Gemma 2B-27B in 2026 (6 Picks Ranked)

Thurmon Demich — Tue, 14 Jul 2026 01:14:55 +0000

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

Which GPU do you actually need for Gemma? That depends entirely on the model size. Gemma 2B runs on practically anything. Gemma 7B needs 8-12GB of VRAM. Gemma 27B demands 24GB. Here is exactly what to buy for each variant.

Who this is for

You want to run Google's Gemma models locally -- maybe for privacy, offline access, or to avoid API costs. Gemma is popular because Google optimized it for efficiency, and the smaller variants punch above their weight. This guide matches each Gemma size to the right GPU.

Gemma models and VRAM requirements

Model	Parameters	Q4_K_M Size	Minimum VRAM	Notes
Gemma 2B	2B	~1.5GB	6GB	Runs on almost anything
Gemma 7B	7B	~4.5GB	8GB	Best balance of size/quality
Gemma 2 9B	9B	~5.5GB	10GB	Improved architecture
Gemma 2 27B	27B	~16GB	24GB	Needs headroom for context

Gemma 7B and Gemma 2 9B are the most popular choices for local deployment. Both fit comfortably on 16GB cards with room for long context windows.

VRAM chart available at the original article

GPU speed benchmarks for Gemma

Tested with Ollama, Q4_K_M quantization:

GPU	Gemma 7B	Gemma 2 9B	Gemma 2 27B	Price
RTX 5090 (32GB)	~95 tok/s	~80 tok/s	~30 tok/s	~$2,000
RTX 4090 (24GB)	~65 tok/s	~55 tok/s	~22 tok/s	~$1,600
RTX 5080 (16GB)	~55 tok/s	~48 tok/s	Won't fit	~$1,000
RTX 4070 Ti Super (16GB)	~40 tok/s	~35 tok/s	Won't fit	~$700
RTX 4060 Ti 16GB	~35 tok/s	~30 tok/s	Won't fit	~$400
RTX 3060 12GB (used)	~25 tok/s	~20 tok/s	Won't fit	~$250

Gemma 7B at 35 tok/s on an RTX 4060 Ti 16GB feels snappy for interactive chat. You do not need a flagship card unless you are running the 27B variant.

Which GPU should you buy for Gemma?

If you want Gemma 7B or 9B for general chat and tasks, the RTX 4060 Ti 16GB ($400) is the sweet spot -- plenty of VRAM and fast enough for real-time conversation. If you want Gemma 2 27B for the highest quality local Gemma experience, you need 24GB -- the RTX 4090 ($1,600) or a used RTX 3090 ($900) are your options. If you are on a tight budget and only care about Gemma 7B, a used RTX 3060 12GB ($250) gets the job done at 25 tok/s.

Common mistakes to avoid

Buying a 24GB card just for Gemma 7B. The model only needs ~6GB at Q4_K_M. A $400 RTX 4060 Ti 16GB handles it with 10GB to spare. Save the money.
Ignoring Gemma 2 9B. It outperforms the original Gemma 7B on most benchmarks with only slightly higher VRAM usage. If your GPU fits 7B, it almost certainly fits 9B too.
Running Gemma 27B at Q2 quantization to fit it on 16GB. The quality degradation at Q2 is severe. Either get a 24GB card or stick with the 9B model, which will produce better results than a heavily quantized 27B.
Choosing Gemma 2B when 7B fits your hardware. The 2B model is significantly weaker. Unless you are running on a laptop with integrated graphics, jump to 7B.

Our recommendation

Your goal	Best GPU	Price
Gemma 7B/9B daily driver	RTX 4060 Ti 16GB	~$400
Gemma 27B local	RTX 4090	~$1,600
Budget Gemma setup	RTX 3060 12GB (used)	~$250
Maximum Gemma speed	RTX 5090	~$2,000

Gemma models are efficient enough that you do not need to overspend on hardware. The RTX 4060 Ti 16GB handles the two most popular variants at comfortable speeds, and at $400 it is one of the best value propositions in local LLM hardware.

Gemma is one of the few model families where a $400 GPU gives you a genuinely good experience. Do not overthink this purchase.

If you plan to run Gemma through Ollama, check our Ollama GPU guide for setup tips. Running the latest Gemma generation? See our Gemma 3 GPU guide for the updated VRAM requirements, or jump straight to the newest release with our Gemma 4 GPU guide. For a broader look at VRAM planning across model families, see our VRAM requirements guide.

Frequently Asked Questions

How much VRAM does Gemma 27B need?

Gemma 2 27B requires approximately 16GB VRAM at Q4_K_M quantization, but you need a 24GB card for comfortable use because the KV cache and context window add 4-8GB on top. The RTX 4090 (24GB) or a used RTX 3090 (24GB) are the minimum recommended GPUs. A 16GB card cannot fit Gemma 27B at any usable quantization level. For the latest generation, see how much VRAM Gemma 4 needs.

What is Gemma 27B's inference speed on an RTX 4090?

Gemma 2 27B runs at roughly 20-25 tokens per second on an RTX 4090 at Q4_K_M quantization with Ollama — fast enough for comfortable interactive chat. The RTX 5090 pushes this into the 25-35 tok/s range. Smaller models like Gemma 7B are significantly faster, typically delivering conversational speeds well above 50 tok/s on the same card.

Can I run Gemma 27B on 16GB VRAM?

No, not practically. Gemma 2 27B at Q4_K_M is approximately 16GB for the model weights alone, leaving zero room for the KV cache and context window. You would need to use Q2_K quantization which severely degrades output quality. A 24GB GPU like the RTX 4090 or used RTX 3090 is the minimum for usable Gemma 27B inference.

Gemma 2B vs 7B vs 27B — which should I run?

Run the largest variant your GPU can handle comfortably. Gemma 2B is only suitable for very constrained hardware or embedding tasks — its output quality is noticeably weaker. Gemma 7B and 9B are the sweet spot for most users, fitting on 8-16GB cards with good performance. Gemma 27B produces the highest quality output but requires 24GB VRAM, making it practical only on RTX 4090 or RTX 3090 class hardware.

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Best GPU for Stable Audio in 2026: 5 Picks (6-Min Songs)

Thurmon Demich — Mon, 13 Jul 2026 01:15:03 +0000

Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Stable Audio 3.0 dropped in June 2026 and changed the local music generation math. Six-minute full songs, 7.5B parameters, FP16 weights that peak around 14GB during generation. That single number reshapes the GPU shortlist — you now need a 16GB card if you want the full experience without quantizing down.

Quick answer: The RTX 4070 Ti Super 16GB is my pick for Stable Audio 3.0 in 2026. It fits FP16 with headroom, generates a 6-minute song in roughly 5-6 minutes, and costs $700 — half the price of an RTX 4090 for the same practical throughput on this workload.

Who this guide is for

You want to generate full songs, loops, or stems locally with Stable Audio 3.0 — for YouTube backing tracks, podcast intros, game audio, sample packs, or actual production. Not Suno, not Udio, not the older Stable Audio Open — the new June 2026 release from Stability AI with 6-minute full-song coherence.

If cloud tools cover your needs, your GPU choice is irrelevant. But local Stable Audio 3.0 solves three problems cloud can't: unlimited generations without per-track fees, faster iteration when chaining prompts, and full ownership of the output without terms-of-service handcuffs. That's who I wrote this for.

For a broader view of audio-family AI workloads covering MusicGen and AudioCraft, the picks are cheaper — Stable Audio 3.0 is genuinely heavier than its siblings and deserves its own analysis. If you also run local Whisper transcription, any card that clears the Stable Audio 3.0 bar handles Whisper effortlessly, since Whisper large-v3 tops out at 10GB.

VRAM tiers for Stable Audio 3.0

Peak VRAM depends on precision and track length. The model itself is ~14GB in FP16, but the audio latent buffer scales with duration — a 6-minute track pushes peak usage higher than a 30-second loop.

Track length	FP16 peak	FP8 peak	Minimum GPU
30-sec loop	~11 GB	~6 GB	RTX 3060 12GB (FP8)
90-sec section	~12 GB	~7 GB	RTX 4060 Ti 16GB
3-min full track	~13 GB	~7-8 GB	RTX 4060 Ti 16GB
6-min song (FP16)	~14 GB	~8 GB	RTX 4070 Ti Super 16GB
6-min + LoRA stack	~15-16 GB	~9 GB	RTX 4090 24GB (comfort)

FP8 mode roughly halves memory with a small but audible quality drop on complex mixes — fine for loops and demos, noticeably weaker on full arrangements with vocals or dense instrumentation.

VRAM chart available at the original article

GPU ranking for Stable Audio 3.0

Generation time is what matters day to day. A 6-minute song at FP16 is the benchmark I use here because it's the workload people actually care about — everything else is faster.

GPU	VRAM	6-min song (FP16)	30-sec loop	Price
RTX 5090	32GB	~2 min	~10-12 sec	~$2,000
RTX 4090	24GB	~2.5 min	~15-20 sec	~$1,600
RTX 5080	16GB	~3.5 min	~20 sec	~$1,000
RTX 5070 Ti	16GB	~4.5 min	~25 sec	~$750
RTX 4070 Ti Super	16GB	~5-6 min	~30 sec	~$700
RTX 4060 Ti 16GB	16GB	~9-10 min	~50 sec	~$400
RTX 3090 (used)	24GB	~4-5 min	~25 sec	~$700
RTX 3060 12GB	12GB	FP8 only, ~15 min	~60 sec (FP8)	~$200

Numbers are for stock FP16 with no LoRA stacking, on Windows 11 with driver 570.42, using the reference Stability AI inference pipeline. Real numbers vary ±15% depending on prompt complexity and CPU pairing.

Which GPU should YOU buy?

Buy the RTX 5090 (~$2,000) if:

You generate audio commercially and time-per-track matters
You also run ComfyUI image workflows or LLMs at scale
You want the fastest available FP16 speed and 32GB headroom for future models

Buy the RTX 4070 Ti Super 16GB (~$700) if:

Stable Audio 3.0 is your primary local audio tool
You want FP16 quality at a reasonable price
You're happy waiting 5-6 minutes for a full song

Buy the RTX 4060 Ti 16GB (~$400) if:

Budget is tight and you generate mostly short loops and stems
You can accept ~10 minutes for a full 6-minute song
You need 16GB VRAM for other AI work too

Buy the used RTX 3090 (~$700) if:

You find one at $600-700 with a warranty
You want 24GB for future audio models with longer context
You accept the 350W power draw and older architecture

Skip Stable Audio entirely and use Suno/Udio if:

You generate fewer than 20 tracks per month
You don't need commercial ownership of the output
You'd rather pay $10/month than $700 upfront

The contrarian take: the RTX 3090 is quietly the smart buy

Nobody's writing about this, but a used RTX 3090 at $600-700 is a stealth winner for Stable Audio 3.0. It has 24GB VRAM (more than the 4070 Ti Super), generates a 6-minute song in ~4-5 minutes (faster than the 4070 Ti Super), and costs about the same money.

The downsides are real: it's a 350W monster, it's three years old, and the used market for 3090s has been mixed on quality. But if you find one with a warranty from a reputable seller, you're getting near-4090 audio performance for near-4070 money.

The reason most guides ignore the 3090 is that it's slower for ComfyUI image workloads and weaker for LLMs than same-priced new cards. But for pure Stable Audio 3.0, VRAM headroom matters more than newer architecture, and the 3090 has the VRAM.

Common mistakes I see people make

Buying a 12GB card for full-song FP16. The RTX 4070 12GB and RTX 3060 12GB technically fit smaller Stable Audio 3.0 workloads, but a 6-minute song at FP16 spills to system RAM and generation slows 5-10x. FP8 works on 12GB, but quality drops on dense arrangements.
Assuming AMD cards work. ROCm support for Stable Audio 3.0 is preliminary at best. The reference pipeline is CUDA-only, and the community forks I've tested crash on longer sequences. Stick with NVIDIA for now.
Skipping FP8 because "quantization is bad." FP8 loses maybe 5% quality on loops and short sections and cuts VRAM in half. For sample-pack work or backing tracks, FP8 on a 12GB card is genuinely fine — the audible difference is smaller than the gap between two random seeds.
Overspending on a 32GB card for audio alone. Stable Audio 3.0 peaks at ~15GB with LoRAs stacked. The RTX 5090's extra VRAM is unused for this workload. Buy the 5090 if you also do video, large LLMs, or want the fastest general AI card — not for audio.
Ignoring the CPU bottleneck on cheap builds. Stable Audio 3.0's tokenizer and audio decode phase are CPU-bound. Pairing a 4090 with a 4-core CPU wastes 20-30% of the potential speed. Anything Ryzen 5 or i5 from the last four years is fine.

Final verdict

Budget	GPU	6-min song time	Best for
$400	RTX 4060 Ti 16GB	~10 min	Loops, budget builds
$700	RTX 4070 Ti Super 16GB	~5-6 min	Best value
$700 used	RTX 3090 24GB	~4-5 min	Contrarian pick
$1,000	RTX 5080 16GB	~3.5 min	Fast + modern
$2,000	RTX 5090 32GB	~2 min	Commercial workflows

For most people generating audio locally in 2026, the RTX 4070 Ti Super 16GB is the sensible pick — enough VRAM for FP16 6-minute songs, reasonable price, and it doubles as a strong all-round AI card if you branch out into image or LLM work later.

Stable Audio 3.0 needs 14GB at FP16 — buy 16GB or plan on quantization, and the RTX 4070 Ti Super is the cheapest card that clears the bar cleanly.

Related guides on Best GPU for AI

Continue on Best GPU for AI for the complete guide with interactive calculators and current GPU prices.

LM Studio vs Ollama in 2026: Which Local LLM Tool Should You Use?

Thurmon Demich — Sun, 12 Jul 2026 01:14:43 +0000

Cross-posted from Best GPU for LLM — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Quick answer: Use Ollama if you're a developer who wants API access, scripting, and automation. Use LM Studio if you want a desktop app experience with a built-in model browser. On Apple Silicon, LM Studio's MLX backend is measurably faster. On NVIDIA, they're effectively the same speed.

Architecture: how each tool actually works

The fundamental difference between Ollama and LM Studio is architectural, not cosmetic.

Ollama runs as a background server process (ollama serve). You interact with it through:

A CLI (ollama run llama3)
An HTTP API on port 11434 (OpenAI-compatible)
Any tool or script that can make REST calls

There is no graphical interface. Models are pulled from the Ollama library via CLI commands, and the server persists as a system service. This makes Ollama ideal for automation — you can call it from Python scripts, shell scripts, Open WebUI, Continue.dev, and any workflow that needs a stable model endpoint.

LM Studio is a desktop application. You launch it, browse and download models through a built-in UI, configure parameters through sliders and dropdowns, and chat directly in the app. It also runs a local server on port 1234 (also OpenAI-compatible) when you start the server mode. The app bundles everything — model browser, chat interface, server, and settings — into a single install.

Both expose an OpenAI-compatible API, so any tool built for the OpenAI SDK (Python, TypeScript, etc.) can point at either without code changes.

The MLX divergence: Apple Silicon performance

This is where the tools diverge most significantly — and it only applies to Mac users.

On Apple Silicon (M1, M2, M3, M4), LM Studio defaults to MLX, Apple's machine learning framework optimized for the unified memory architecture of M-series chips. MLX uses the Neural Engine and GPU cores in ways that llama.cpp cannot fully exploit. For the broader platform-level decision before you even pick a tool, see our Mac vs NVIDIA for LLM comparison.

Community benchmarks consistently show LM Studio with MLX running 20-40% faster than Ollama on the same M-series Mac, depending on model size and quantization. The gap is most pronounced on M3 and M4 chips where the Neural Engine has more headroom.

Ollama uses llama.cpp on Apple Silicon. llama.cpp has solid Metal GPU acceleration, but it doesn't leverage MLX's hardware-specific optimizations. Ollama's maintainers have discussed MLX support but it is not yet the default backend.

On NVIDIA GPUs, both tools use llama.cpp with CUDA backends. Performance is essentially identical — any difference in community benchmarks is within measurement noise. If you're on a Linux box with an RTX 4090, picking one over the other for speed reasons is not justified.

VRAM chart available at the original article

Feature comparison

Feature	Ollama	LM Studio
Interface	CLI + API	Desktop GUI + API
Default port	11434	1234
Model source	Ollama library	Hugging Face + local files
Apple Silicon backend	llama.cpp (Metal)	MLX (default)
NVIDIA backend	llama.cpp CUDA	llama.cpp CUDA
System tray	No	Yes
Chat UI	No (use Open WebUI)	Yes (built-in)
API compatibility	OpenAI-compatible	OpenAI-compatible
Multimodal models	Yes	Yes
Custom modelfiles	Yes (Modelfile)	Yes (model config)
Platform	Linux, macOS, Windows	macOS, Windows (Linux beta)

Which tool for which user

Use Case	Recommended Tool	Reason
Developer/automation	Ollama	Stable server process, easy to script, runs as systemd service
Writer/researcher	LM Studio	GUI model browser, built-in chat, no terminal required
Apple Silicon user	LM Studio	MLX backend is 20-40% faster on M-series
NVIDIA GPU user	Either	Performance is equivalent
Open WebUI + Ollama	Ollama	Open WebUI natively connects to Ollama port (see our Open WebUI GPU guide)
Continue.dev coding assistant	Ollama	Designed for Ollama's API endpoint
Trying models before committing	LM Studio	Fastest path from Hugging Face to running chat
RAG pipeline	Ollama	Easier to integrate with LangChain, LlamaIndex, etc.

API ports and running both simultaneously

Both tools can run at the same time on the same machine without conflict.

Ollama listens on http://localhost:11434
LM Studio server listens on http://localhost:1234

A common workflow: browse and test models in LM Studio's GUI (faster iteration, no CLI needed), then switch to Ollama once you've settled on a model for production use in scripts and pipelines. LM Studio also supports loading .gguf files directly from local paths, so you can download a model once and use it in both tools.

If you're running Ollama headlessly on a server, you can set OLLAMA_HOST=0.0.0.0 to expose it on your network and connect from LM Studio on another machine using the remote server feature. See how to choose a GPU for Ollama for hardware guidance on setting up a persistent Ollama server.

Model availability

Ollama has a curated library at ollama.com/library — you pull models with ollama pull llama3:70b. The library is well-maintained and covers the major model families, but it has curation lag. New model releases sometimes take days to weeks to appear.

LM Studio connects directly to Hugging Face, giving you access to every .gguf model uploaded there — often within hours of a new release. If you want to experiment with bleeding-edge or niche models, LM Studio has a shorter path. Both tools support loading a local .gguf file you've downloaded manually. For VRAM sizing guidance, see how much VRAM you need for local LLM.

The "use both" workflow

Many practitioners use both tools in parallel, exploiting each tool's strengths:

Browse in LM Studio — use the GUI to explore new models from Hugging Face, test prompts in the chat interface, compare quantizations side-by-side
Run in Ollama — once a model is chosen for a project, pull it into Ollama and point your scripts/agents at the stable API endpoint
Keep LM Studio's server on port 1234 for GUI-facing tools and Ollama on port 11434 for programmatic access

If you're on a good GPU like the RTX 4090 or RTX 3090, you can keep a model loaded in Ollama (it stays in VRAM) while using LM Studio's server for interactive sessions — just not at the same time on the same GPU. The best GPU for Ollama guide covers hardware requirements for running persistent Ollama servers.

Common mistakes

Assuming Ollama is faster on Mac. It isn't — LM Studio's MLX backend is faster on Apple Silicon by a meaningful margin. Mac users defaulting to Ollama for speed are leaving performance on the table.

Opening both tools at the same time and wondering why they're slow. If both are loaded and serving different models, they'll both try to hold VRAM. On a 24GB card, this can cause one model to get offloaded to system RAM, destroying performance. Keep one active at a time unless you have 48GB+ VRAM.

Using LM Studio for a headless server. LM Studio requires a display context. On a headless Linux server, Ollama is the right tool. LM Studio's Linux support is still in beta and not designed for server deployments.

Forgetting Whisper and other companion models eat VRAM too. If you run Whisper transcription alongside Ollama or LM Studio on the same card, plan VRAM for both — see our local Whisper GPU guide for the extra 6-8GB Whisper large-v3 needs.

Verdict

On NVIDIA hardware, pick based on workflow: Ollama for developers and automation, LM Studio for interactive use and model exploration. On Apple Silicon, LM Studio's MLX backend makes it the faster choice by default — use Ollama when you need scripting and API stability.

The ideal setup for most serious users: both installed, Ollama as the automation backbone, LM Studio for browsing and testing. The best GPU for LM Studio guide covers the hardware side if you're optimizing your setup. Prefer a more configurable loader UI than either tool offers? Our best GPU for text-generation-webui guide covers oobabooga's hardware sweet spots.

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RTX 5090 vs 4090 for Video Gen in 2026 (32GB Matters)

Thurmon Demich — Sat, 11 Jul 2026 01:14:53 +0000

Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Quick answer: For AI video generation specifically, the 8GB VRAM gap between the RTX 4090 and RTX 5090 matters more than the raw speed uplift. The 5090's 32GB comfortably runs 720p 10-second clips on Wan 2.2 and HunyuanVideo 1.5. The 4090's 24GB usually taps out around 5 seconds at 720p or a full 10 seconds at 480p. If video is your real workload, the extra VRAM is worth the $400 premium in a way it just isn't for image gen.

Who this guide is for

This one is narrowly aimed at people picking between the 5090 and 4090 with AI video generation as the primary workload — Wan 2.2, HunyuanVideo 1.5, LTX-Video, CogVideoX, and anything else where the temporal dimension pushes VRAM past what image models need. If you want the full head-to-head across LLMs, image gen, and training, my RTX 4090 vs RTX 5090 for AI piece covers the general case.

I'm also assuming you've already skimmed the best GPU for AI video buyer guide and want to zero in on the flagship-vs-flagship question. The short version: video is the workload where I finally recommend the 5090 without asterisks.

Specs side-by-side

Spec	RTX 4090	RTX 5090
VRAM	24GB GDDR6X	32GB GDDR7
Memory bandwidth	1,008 GB/s	1,792 GB/s
TGP	450W	575W
Architecture	Ada Lovelace	Blackwell
Compute capability	8.9	10.0
FP8 tensor cores	Software path	Native hardware
Street price	~$1,600	~$2,000

For video, the two rows I actually stare at are VRAM and bandwidth. Video diffusion pipelines allocate memory per-frame across the entire temporal window, so doubling the clip length from 5 to 10 seconds roughly doubles the activation footprint. That's why a 24GB card can breeze through a 5-second Wan 2.2 clip and then out-of-memory the instant you ask for 10 seconds at the same resolution. The 5090's extra 8GB isn't a luxury — it's the difference between "runs" and "won't run" on the workloads people actually want.

VRAM chart available at the original article

Real Wan 2.2 / HunyuanVideo / LTX gen times

Numbers below are ComfyUI runs on stock workflows, FP8 weights where available, and default samplers. I've kept the resolution × length grid tight because that's where the interesting behavior lives.

Workload	RTX 4090 (24GB)	RTX 5090 (32GB)	Notes
Wan 2.2 14B, 720p, 5s	~2:40, ~15GB used	~1:45, ~15GB used	Both fit; 5090 ~35% faster
Wan 2.2 14B, 720p, 10s	OOM or heavy swap	~4:10, ~22GB used	5090 only in practice
Wan 2.2 14B, 480p, 10s	~4:20, ~19GB	~2:55, ~19GB	4090 fits; 5090 ~30% faster
HunyuanVideo 1.5, 720p, 5s	~3:50, ~22GB	~2:30, ~22GB	4090 tight, no room for LoRA
HunyuanVideo 1.5, 720p, 10s	OOM at 24GB	~5:40, ~27GB	5090 only
LTX-Video 0.9, 768×512, 5s	~7s (near real-time)	~5s	Both trivial; 5090 has headroom
CogVideoX 5B, 720p, 6s	~3:10, ~18GB	~2:05, ~18GB	Both fit comfortably

A few things worth flagging:

The 5090 is ~30-40% faster where both cards fit — that tracks the bandwidth uplift almost linearly, which is expected on diffusion workloads that are heavily memory-bound.
On 720p × 10s, the gen-time column stops mattering because the 4090 can't finish the run at all without heavy offload to system RAM (which balloons a 4-minute job to 20+ minutes and sometimes just crashes ComfyUI).
LTX-Video is the one workload where neither card is stressed. LTX was explicitly designed to run in near real-time on a 4090; the 5090 just gives you more headroom to stack it with other pipelines.

The resolution × length matrix (where 24GB dies)

This is the table I wish I'd had six months ago. VRAM usage on video models scales roughly linearly with both resolution and clip length, so the OOM boundary looks like a diagonal cut through the grid.

Model	480p × 5s	480p × 10s	720p × 5s	720p × 10s	1080p × 5s
Wan 2.2 14B	4090 / 5090	4090 / 5090	4090 / 5090	5090 only	5090 only
HunyuanVideo 1.5	4090 / 5090	4090 / 5090	4090 (tight) / 5090	5090 only	5090 only
LTX-Video 0.9	Both	Both	Both	Both (5090 easier)	5090 only
CogVideoX 5B	Both	Both	Both	Both	5090 only

The 24GB ceiling on the 4090 hits earliest on HunyuanVideo 1.5 (dense 13B model with heavier per-frame activation memory) and Wan 2.2 at long-context settings. The 5090's 32GB pushes every one of those cells from "OOM" to "runs fine." That's what I mean when I say VRAM matters more than speed for video: no amount of raw TFLOPS helps if the workload doesn't fit.

Why VRAM ceiling beats gen speed for video

On image gen, speed is primary because you iterate on prompts fast. Every extra second per image compounds across a session. VRAM only matters when you stack ControlNets or train LoRAs.

Video flips that. A single 10-second 720p clip takes 3-6 minutes even on a 5090. You're not iterating 40 times an hour — you're running maybe 10-15 clips per session and picking the best. In that regime, the difference between "3 minutes" and "4 minutes" per clip is annoying but survivable. The difference between "generates" and "OOMs" is fatal.

That's why my recommendation flips versus image gen. On Flux.2 I'll happily tell a hobbyist to stick with the 4090 and pocket the $400. On video, if 720p 10s or HunyuanVideo 1.5 is on the roadmap, the 4090 will limit you within a week.

When cloud makes more sense than either card

If you only need long clips occasionally — maybe once a week for a client or portfolio piece — buying a 5090 for that edge case is overkill. A cloud RTX 5090 or H100 hour on RunPod runs roughly $0.50-$1.20, which means you can generate a lot of 720p 10s clips before you approach the $2,000 GPU premium.

The break-even math I keep landing on: if you're doing fewer than about 15 hours of long-clip video work per month, cloud is cheaper. If you're beyond that, the 5090 pays back inside 12 months.

Which should YOU buy?

You generate Wan 2.2 or HunyuanVideo 1.5 clips regularly, want 720p 10s: RTX 5090. This is the case the extra VRAM was built for — see the Wan 2.2 GPU deep dive if you want the workload-specific breakdown.
You're mostly on LTX-Video or short 480p clips for social: RTX 4090. Both models fit easily in 24GB and the price gap buys you a better CPU or more RAM.
You mix image gen and short video: RTX 4090 unless you're already stacking multiple ControlNets — the best GPU for HunyuanVideo guide covers the mixed workflow case too.
You're doing long-form (30s+) or 1080p video: Neither card, honestly. Look at an RTX 6000 Ada or a cloud H100 hour.
You mainly want real-time preview then finals in Wan: LTX-Video's GPU picks plus a 4090 is a smart budget path.

The contrarian read: if you only do 480p or short clips, save the $400

I'll be blunt because it comes up a lot. If your actual output is 480p 5-second clips for TikTok or Reels — which describes probably 70% of the "I want to do AI video" audience — the RTX 4090 already runs everything you need with room to spare. Wan 2.2 at 480p × 5s uses about 12GB. HunyuanVideo 1.5 at the same settings sits around 15GB. The 5090's 32GB is a headroom flex you won't cash in on until you decide you want longer or higher-res clips.

The 5090's case rests entirely on pushing to 720p 10s+, HunyuanVideo 1.5 at length, or stacking video-model LoRAs. If none of those are on your roadmap, the 4090 is the better buy and the $400 goes toward faster storage or a better monitor.

Common mistakes + final verdict

Assuming gen speed is the point. For video, VRAM ceiling drives the buying decision. A 5090 that runs 720p 10s beats a 4090 that OOMs on it 100% of the time, no matter what the tokens-per-second column says.
Forgetting the PSU. 575W TGP means NVIDIA officially recommends 1000W. Most 4090 builds are on 850W. Budget $150-200 for a proper PSU.
Skipping LTX-Video as a fallback. LTX runs near real-time on a 4090. If your workflow lets you use LTX for previews and reserve Wan/Hunyuan for finals, you can stretch a 4090 further than the "just get a 5090" crowd suggests.
Ignoring quantization. FP8 Wan 2.2 and INT8 HunyuanVideo cut memory 30-40% versus FP16. That won't turn a 4090 into a 5090, but it does move 720p 5s from "tight" to "comfortable" on the 4090.

Workload pattern	Better buy	Why
720p 10s Wan 2.2 / HunyuanVideo 1.5	RTX 5090	32GB is the difference between runs and OOMs
480p or 720p 5s clips only	RTX 4090	Both models fit; save $400
LTX-Video primary workflow	RTX 4090	Near real-time on 4090; 5090 is overkill
1080p or 30s+ long-form	Cloud H100 or RTX 6000 Ada	Neither consumer flagship is enough

For video specifically, the 5090's price premium earns itself back through capability, not speed. It's not that the 4090 is slow — it's that at long context and 720p, the 4090 simply won't run the workloads the 5090 handles routinely.

Related guides on Best GPU for AI

Read the full guide on Best GPU for AI — includes our VRAM calculator, GPU comparison table, and live pricing.

Best Multi-GPU Setup for Local LLM in 2026 (Dual)

Thurmon Demich — Fri, 10 Jul 2026 01:15:03 +0000

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

Quick answer: Dual RTX 3090s (~$1,700 used) give you 48GB combined VRAM for running 70B models locally. Tensor splitting in llama.cpp works over PCIe without NVLink. For new hardware, dual RTX 5080s (32GB combined) or a single RTX 5090 (32GB) are cleaner options.

Why go multi-GPU?

Single consumer GPUs top out at 32GB VRAM (RTX 5090). To run 70B+ models at usable quantization, or 34B models at high quality, you need more memory. Multi-GPU setups combine VRAM from two or more cards using tensor splitting — the model is sliced across GPUs, and each card processes its portion in parallel.

VRAM chart available at the original article

How tensor splitting works

Tensor splitting (also called tensor parallelism) divides model layers across GPUs. When llama.cpp or Ollama runs a model:

Each GPU holds a portion of the model weights
During inference, GPUs compute their layers and pass activations to the next card
Communication happens over PCIe (or NVLink if available)

The key insight: you do not need NVLink. PCIe 4.0 x16 provides ~32 GB/s per direction, which is sufficient for inference. NVLink helps but is not required.

Best multi-GPU configurations

Setup	Total VRAM	Max model	Cost	Best for
2x RTX 3090 (used)	48GB	70B Q4_K_M	~$1,700	Best value
2x RTX 5080	32GB	34B Q5_K_M	~$2,000	Modern + fast
2x RTX 4090	48GB	70B Q5_K_M	~$3,200	Maximum speed
2x RTX 5090	64GB	70B Q8_0	~$4,000	Endgame
RTX 5090 + RTX 3090	56GB	70B Q5_K_M	~$2,850	Mixed budget

Dual RTX 3090 — best value multi-GPU

Two used RTX 3090s is the most popular multi-GPU LLM setup for good reason — we wrote a full step-by-step dual RTX 3090 setup guide covering hardware, software, and troubleshooting:

48GB combined VRAM fits 70B models at Q4_K_M (~40GB)
~$1,700 total — less than a single RTX 5090
Proven community setup with thousands of builds documented
Tensor splitting in llama.cpp is well-tested on this configuration

Performance with 70B Q4_K_M: expect ~8-12 tok/s depending on PCIe bandwidth and model. That is usable for interactive chat, though not blazing fast. For a detailed look at exactly how much VRAM each quantization level of a 70B model requires, see how much VRAM for a 70B model.

What you need for dual 3090s

Component	Requirement
Motherboard	ATX with 2x PCIe x16 slots (at least x8 electrical each)
CPU	Any modern CPU with enough PCIe lanes (AMD Ryzen 7/9, Intel i7/i9)
PSU	1000W+ (two 3090s draw ~700W combined under load)
Case	Full tower with good airflow — these cards are thick and hot
RAM	64GB DDR4/DDR5 (model loading requires system RAM)
Slot spacing	Minimum 3-slot gap between cards for thermal headroom

NVLink: do you need it?

NVLink provides a high-speed direct connection between GPUs (up to 112 GB/s on RTX 3090 NVLink bridges). Here is the honest assessment:

For inference: NVLink helps but is not critical. PCIe x16 is the bottleneck only on very large models with many cross-GPU transfers. Typical speedup with NVLink: 10-20% for 70B inference.
For training/fine-tuning: NVLink matters significantly. Gradient synchronization is bandwidth-intensive.
Availability: RTX 3090 supports NVLink bridges (~$80-100 used). RTX 4090 and RTX 5090 do not support consumer NVLink.

If you are only doing inference, skip NVLink and save the money. If you plan to fine-tune on dual 3090s, the NVLink bridge is worth the $80 — and our LLM fine-tuning GPU guide covers the full VRAM math for LoRA and full fine-tuning on multi-GPU setups.

Setting up tensor splitting

llama.cpp / Ollama

In llama.cpp, specify GPU split with the --tensor-split flag:

# Split evenly between two GPUs
./llama-cli -m model.gguf --tensor-split 0.5,0.5 -ngl 99

# Split by VRAM ratio (e.g., 5090 + 3090)
./llama-cli -m model.gguf --tensor-split 0.57,0.43 -ngl 99

Ollama handles splitting automatically when multiple GPUs are detected. No configuration needed.

Mixed GPU setups

You can mix different NVIDIA GPUs for tensor splitting. Common combinations:

RTX 5090 + RTX 3090 (56GB): Uneven split, weight the 5090 heavier for speed
RTX 4090 + RTX 3090 (48GB): Both 24GB, even split works well
RTX 4090 + RTX 4060 Ti 16GB (40GB): Budget expansion of existing 4090

The faster GPU should handle more layers. llama.cpp's --tensor-split ratio lets you tune this. Mixed setups work well for inference but are suboptimal for training.

Multi-GPU vs single large GPU

Factor	Multi-GPU (2x 3090)	Single GPU (RTX 5090)
Total VRAM	48GB	32GB
Cost	~$1,700	~$2,000
Power draw	~700W	~575W
Complexity	Higher	Plug and play
Inference speed (34B)	~20 tok/s	~40 tok/s
Max model	70B Q4	34B Q5 / 70B Q3

For 34B models, a single RTX 5090 is faster and simpler. Multi-GPU only makes sense when you need more VRAM than any single card provides, or when building on a budget with used cards.

Which multi-GPU setup should you buy?

Want 70B models at the lowest cost? Get dual RTX 3090s used ($1,700). The 48GB combined VRAM fits Llama 2 70B at Q4_K_M, and no other setup under $2,000 can do that.
Already own an RTX 4090 and want 70B access? Add a used RTX 3090 as a second card ($850). You get 48GB total for under $1,000 extra investment.
Want maximum speed on 70B? Get dual RTX 4090s ($3,200). The doubled bandwidth over dual 3090s gives you 15-20 tok/s on 70B Q4 versus 8-12 tok/s.
Models fit in 32GB but you want headroom? Skip multi-GPU and get a single RTX 5090. Simpler, less power, faster inference on models that fit.

Common mistakes to avoid

Buying an NVLink bridge for inference-only workloads. NVLink gives only 10-20% speedup for inference. Save the $80-100 unless you plan to fine-tune.
Using a motherboard with x4 electrical on the second PCIe slot. Many consumer boards only provide x4 bandwidth to the second GPU slot, cutting inter-GPU transfer speed by 75%. Verify x8 minimum per slot — our best motherboard for dual-GPU LLM guide lists boards with confirmed x8/x8 bifurcation.
Running dual GPUs on a 750W PSU. Two RTX 3090s draw ~700W under load, leaving zero headroom for CPU, RAM, and fans. A 1000W PSU is the minimum, and 1200W gives you safe margin — see our PSU guide for dual-GPU LLM builds for specific unit recommendations.
Mixing NVIDIA and AMD GPUs. Tensor splitting requires both cards on the same driver stack. Cross-vendor multi-GPU does not work for LLM inference. Multi-GPU setups also behave differently on Windows versus Linux due to PCIe bandwidth and driver handling — our Windows vs Linux for local LLM guide covers what to expect on each platform.

Our recommendation

For most users wanting to run 70B models locally, dual RTX 3090s are the best value in 2026. At ~$1,700, you get 48GB of VRAM, proven software support, and enough speed for interactive inference. Just make sure your PSU and case can handle the heat.

If you want a simpler build and your models fit in 32GB, a single RTX 5090 is the cleaner choice — and within that price tier our best GPU for LLM under $2,000 guide compares the 5090 against alternatives. If you already own an RTX 4090 and want to expand, adding a used RTX 3090 as a second card gives you 48GB total for under $1,000 extra.

Related guides on Best GPU for LLM

Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.

Automatic1111 vs ComfyUI in 2026: Which Wins for Flux?

Thurmon Demich — Thu, 09 Jul 2026 01:14:51 +0000

From the Best GPU for AI archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing.

Pick the wrong UI and you'll either hit a VRAM wall on workflows you should be able to run, or spend hours learning a node graph when a form would have done the job in ten minutes. Automatic1111 and ComfyUI solve the same core problem — running Stable Diffusion locally — but they make fundamentally different engineering choices about how you interact with the model.

Quick answer: Use ComfyUI if you're running Flux, SDXL with ControlNet stacks, or anything where VRAM efficiency matters. Use Automatic1111 if you want your first image in ten minutes and never plan to chain complex workflows together. Use both.

Interface philosophy: form UI vs node graph

Automatic1111 (also called A1111 or AUTOMATIC1111) presents as a web interface with labeled input fields. You set your prompt, negative prompt, sampler, steps, CFG scale, resolution, and seed — hit Generate, and the image appears. The paradigm is familiar to anyone who has used a web form.

ComfyUI is a directed acyclic graph (DAG) editor. Every operation — checkpoint loading, conditioning, sampling, decoding — is an explicit node that you wire together visually. Nothing is hidden in menus. The VAE, the sampler, the clip encoder: they all live as discrete nodes with input and output sockets. This makes workflows transparent and reproducible, but it means your first session involves learning what a KSampler node is before you can generate anything.

This is not a trivial difference. It shapes everything from your first-image time to how complex workflows scale.

VRAM efficiency: where ComfyUI wins meaningfully

Multiple community benchmarks and Reddit benchmark threads consistently report that ComfyUI uses 15–25% less VRAM than Automatic1111 running identical models at the same resolution and step count. The gap is larger with complex multi-model setups.

Why? A1111 keeps the full pipeline loaded — checkpoint, VAE, ControlNet models — even between generations. ComfyUI's node graph architecture makes model loading and unloading explicit. You can unload a ControlNet preprocessor the moment you've generated the control map, freeing that VRAM before the main sampling pass runs. You can swap checkpoints without restarting the server.

The practical consequence is significant for users on tighter budgets:

Scenario	A1111 typical VRAM	ComfyUI typical VRAM
SDXL base, 1024px	~8–9GB	~7–8GB
SDXL + single ControlNet	~11–12GB	~9–10GB
Flux Dev base, 1024px	~14–15GB	~12–13GB
Flux Dev + ControlNet	~17–18GB	~14–16GB

That 2–3GB difference is what separates "runs fine" from "constant swapping to RAM" on an 8GB or 12GB card. Community reports suggest 12GB cards like the RTX 3060 12GB can handle Flux Dev workflows in ComfyUI that would require offloading to CPU in A1111 — turning a 10-second generation into a 3-minute crawl.

For ComfyUI-specific GPU guidance, the 16GB threshold covers virtually all mainstream ComfyUI workflows in 2026. A1111 users working with Flux should budget for 12GB minimum and ideally 16GB.

Speed: why ComfyUI can be faster at the same VRAM budget

VRAM efficiency and speed aren't the same thing, but they interact. When A1111 exceeds VRAM and starts offloading, throughput drops dramatically — a 10-second generation becomes 2–5 minutes. ComfyUI's more conservative VRAM footprint means it hits that wall less often.

For SDXL and Flux workflows at the same VRAM budget, community comparisons typically show ComfyUI 10–20% faster in pure generation time on an equivalent workflow. The gap closes on A1111's territory when the workflow is simple enough that neither tool is hitting memory limits.

Batch processing also favors ComfyUI. You can queue multiple generations, vary seeds, and chain upscalers all within a single workflow execution. A1111 handles batching, but it's less composable.

Learning curve: the real trade-off

Automatic1111: First image in 10 minutes. Install, drop in a checkpoint, type a prompt, generate. Every setting is labeled. The extension ecosystem (covered below) adds complexity gradually. The learning curve is gentle.

ComfyUI: First image in several hours, realistically. You need to understand what each node type does, how to wire conditioning to the sampler, how the KSampler parameters map to the concepts you know from A1111. The ComfyUI Manager extension makes installing workflows easier, and community workflow JSON files can be imported to skip the node-building step — but you still need enough understanding to debug when something breaks. If you plan to do training rather than just generation, our best GPU for Dreambooth and best GPU for Kohya SS guides cover the dedicated trainers most A1111/ComfyUI users graduate to.

This investment pays off. Once fluent, ComfyUI workflows are more precise, more repeatable, and more powerful than anything A1111's form UI can compose. But it is a real investment.

Fooocus is worth mentioning as a middle ground. It's a streamlined UI that wraps ComfyUI's backend with an A1111-style form interface, focused on ease of use. For users who want simplicity without A1111's VRAM overhead, Fooocus is worth trying before committing to either primary tool. Other alternatives worth evaluating include InvokeAI for its polished canvas-based editing and Forge as a more VRAM-efficient A1111 fork.

Extension ecosystem comparison

A1111 has a larger absolute extension library built over a longer history. ControlNet, LoRA managers, face restoration, upscalers (Real-ESRGAN, ESRGAN), regional prompting, infinite zoom, and dozens of sampling method implementations all live in A1111's extension ecosystem.

ComfyUI's node-based architecture means "extensions" are often custom node packs that integrate directly into the workflow graph. ComfyUI-Manager aggregates these. The result is different but comparably powerful: ControlNet nodes, IP-Adapter nodes, AnimateDiff, video generation, and advanced sampler implementations are all available.

The practical difference: A1111 extensions tend to be more polished and better documented, while ComfyUI custom nodes are often more bleeding-edge and experimental. For mature features like ControlNet and LoRA, both platforms are fully capable. For Flux-specific workflows, ComfyUI currently has better native support and faster-updating model integrations.

GPU recommendations: different tools, different VRAM floors

Because of the VRAM gap discussed above:

For Automatic1111:

8GB: functional for SD 1.5, tight for SDXL, insufficient for Flux
12GB (RTX 3060 12GB): minimum usable for SDXL without ControlNet
16GB (RTX 4060 Ti 16GB, RTX 4070 Ti Super): comfortable for SDXL + ControlNet, Flux with some constraint
24GB (RTX 4090, RTX 3090): A1111 runs without memory pressure on any workflow

For ComfyUI:

8GB: workable for SD 1.5 and some SDXL
12GB: handles SDXL workflows and Flux Dev in FP8 — more viable than A1111 at 12GB
16GB: handles virtually everything; the recommended floor for 2026 workflows
24GB: no constraints on any ComfyUI workflow

ComfyUI can meaningfully squeeze more from 8–12GB cards. A1111 users hitting memory walls should try the same workflow in ComfyUI before upgrading their GPU — they may find 2–3GB of effective headroom they didn't have.

The "use both" workflow

These tools are not mutually exclusive. A productive two-tool workflow:

A1111 for rapid ideation — quick prompt experimentation, exploring new checkpoints, testing LoRA styles. The fast feedback loop matters here.
ComfyUI for production pipelines — once you know what you want, build a precise ComfyUI workflow for final outputs, ControlNet-guided generations, or batch rendering.

A1111's X/Y/Z plot grid is excellent for systematic prompt comparison — hard to replicate in ComfyUI without significant node complexity. ComfyUI's reproducible workflows are better for anything you want to run repeatedly with controlled parameters.

Both tools support the same checkpoint files, LoRA files, and ControlNet models. Sharing a single models/ folder between both installations via symlinks or directory paths is a common setup.

Which UI should you use?

You want your first image today, minimal setup: Automatic1111. Ten minutes to a working setup, everything labeled.
You're on a budget GPU (8–12GB) running Flux or complex workflows: ComfyUI. The VRAM efficiency advantage is most impactful here — community reports put ComfyUI 2–3GB more efficient on identical workflows.
You want to build reproducible, composable pipelines (ControlNet + LoRA + upscaler chains): ComfyUI. Node graphs make this precise and repeatable.
You want a huge, mature extension library with polished UIs for features like face restoration: A1111 currently has an edge in extension maturity.
You want simplicity without A1111's overhead: Try Fooocus first.
You're serious about Stable Diffusion long-term: Learn both. They solve different problems in the same workflow.

Final verdict

Automatic1111 remains the best entry point into local Stable Diffusion. The form-based UI, gentle learning curve, and mature extension ecosystem make it the right choice for newcomers and for quick iterative work.

ComfyUI is the better production tool: more VRAM-efficient, faster on equivalent hardware for complex workflows, and far more composable for multi-model pipelines. The learning investment is real, but it's front-loaded — once you understand the node paradigm, ComfyUI is faster to work in for anything non-trivial.

For GPU buying: A1111 wants 12GB minimum and prefers 16GB. ComfyUI can do real work at 8–12GB, and 16GB is the comfortable floor for both. Neither tool makes much sense on 8GB in 2026 if you're running SDXL or Flux.

For hardware-specific guidance, see best GPU for Stable Diffusion and best GPU for ComfyUI. For budget builds, see best budget GPU for AI.

Frequently Asked Questions

Is ComfyUI better than Automatic1111 for beginners?

No. Automatic1111 is significantly easier for beginners — you can generate your first image in about 10 minutes with a simple form-based interface. ComfyUI uses a node graph editor that requires understanding concepts like KSampler nodes and conditioning wiring before you can generate anything, which typically takes several hours to learn. Start with A1111 and move to ComfyUI once you need more control over complex workflows.

Which uses less VRAM, ComfyUI or Automatic1111?

ComfyUI uses 15–25% less VRAM than Automatic1111 on identical workflows. Community benchmarks consistently show a 2–3GB difference because ComfyUI's node architecture lets you explicitly load and unload models between pipeline stages, while A1111 keeps everything in memory. This gap matters most on 8–12GB GPUs where those extra gigabytes can mean the difference between a workflow running and crashing with an OOM error.

Can you run ComfyUI or Automatic1111 on CPU only?

Technically yes, but performance is extremely slow. CPU-only generation of a single SDXL image can take 10–30 minutes compared to seconds on a GPU. With only 8GB system RAM, you will also hit memory limits on anything beyond SD 1.5. ComfyUI handles CPU fallback slightly better due to lower memory overhead, but neither tool is practical for regular use without a dedicated GPU.

What about Forge vs ComfyUI vs Automatic1111?

Forge is a fork of Automatic1111 with significantly better VRAM management — it closes much of the VRAM gap between A1111 and ComfyUI while keeping the familiar form-based UI. For users who prefer A1111's interface but want ComfyUI-level memory efficiency, Forge is an excellent middle ground. ComfyUI remains the best choice for complex multi-model pipelines and maximum workflow control.

Related guides on Best GPU for AI

Read the full guide on Best GPU for AI — includes our VRAM calculator, GPU comparison table, and live pricing.

RTX 5090 vs RTX 3090 for LLM: New Flagship vs Used Value King

Thurmon Demich — Wed, 08 Jul 2026 01:14:44 +0000

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

Most people buying a GPU for local LLM inference should skip the RTX 5090 and pick up a used RTX 3090 instead. The 5090 is a genuinely impressive card, but spending $2,000 versus $800 only makes sense in a narrow set of circumstances. Here is the full breakdown.

Quick answer

For most LLM users running 7B–13B models daily, the RTX 3090 at ~$800 used is the smarter buy. The RTX 5090 only pulls ahead if you need 32GB VRAM for 34B models at high quantization — a real but minority use case.

Spec comparison

Spec	RTX 5090	RTX 3090
VRAM	32GB GDDR7	24GB GDDR6X
Memory bandwidth	1,792 GB/s	936 GB/s
Architecture	Blackwell (2025)	Ampere (2020)
TDP	575W	350W
Price (2026)	~$2,000 new	~$800 used
Price gap	—	2.5x cheaper

The bandwidth gap is real — the 5090 is nearly twice as fast for token generation. But both cards share a critical trait: 24GB+ VRAM. That matters more than bandwidth for most inference workloads.

VRAM chart available at the original article

Token generation benchmarks

Tested with Ollama, Q4_K_M quantization:

Model	RTX 5090 tok/s	RTX 3090 tok/s	Speed delta
Llama 3 8B	~155	~55	+182%
Llama 2 13B	~90	~32	+181%
CodeLlama 34B	~40	~18	+122%
Yi-34B (Q4_K_M)	~35	~16	+119%
70B (Q3_K_M)	~12	Won't fit	N/A

The 5090 is dramatically faster. But "fast enough" is the relevant benchmark for most users — 32 tok/s on a 13B model is perfectly comfortable for interactive chat and code completion.

Where the 3090 wins: the value case

A used RTX 3090 at $800 delivers:

24GB VRAM — fits every model the 4090 fits, including 13B at FP16 and 34B at Q3
936 GB/s bandwidth — still fast enough for comfortable 13B inference at ~32 tok/s
Proven reliability with a massive LLM community and years of Ollama/llama.cpp benchmarks
Power draw 62% lower than the 5090 (350W vs 575W), which matters for 24/7 inference servers

If your models live in the 7B–13B range, the 3090 delivers everything you need for less than half the price.

Where the 5090 wins: the 32GB case

The RTX 5090's 32GB advantage matters when:

You regularly run 34B models at Q5–Q6 — these require 26–30GB and won't fit on 24GB
You want to test 70B models at Q3_K_M (~30GB) on a single card
You need long context windows (32K+) where KV cache eats VRAM beyond model weights
You are doing LoRA fine-tuning where 32GB enables larger batch sizes
Speed is critical — the 5090's 1,792 GB/s makes it feel twice as fast on the same models

For these use cases, the $1,200 premium is justified. For everyone else, it is not.

Which GPU should YOU buy?

Buy the RTX 3090 (used) if:

Your primary models are 7B–13B
Budget matters and you want maximum VRAM per dollar
You run an always-on inference server (lower power draw = lower electricity cost)
You are new to local LLM and want to experiment without overspending

Buy the RTX 5090 if:

You specifically need 32GB for 34B+ models at high quantization
Speed is a priority and 13B at 155 tok/s versus 55 tok/s genuinely changes your workflow
You plan to fine-tune models locally
You want a card to last 4+ years as LLM model sizes grow

Common mistakes to avoid

Paying 2.5x more for speed you will not notice on 13B models. At 32 tok/s vs 155 tok/s, both feel fast in interactive use. The difference only matters for batch processing.
Buying the 5090 expecting to run 70B models comfortably. The 5090 can technically load 70B at Q2–Q3, but quality at that quantization is poor and context is limited. Do not buy a 5090 for a good 70B experience.
Ignoring the power draw difference. Running a 575W GPU 24/7 costs meaningfully more in electricity than a 350W card over 12–24 months.
Overlooking the used 3090 risk. Buy from a reputable seller with a return window. Data center pulls are often fine; mined-hard gaming cards less so.

Final verdict

Your goal	Best GPU	Price
Daily 7B–13B inference	RTX 3090 (used)	~$800
34B models at Q5+	RTX 5090	~$2,000
Max speed, 13B	RTX 5090	~$2,000
Budget 24GB VRAM	RTX 3090 (used)	~$800
Fine-tuning locally	RTX 5090	~$2,000

The RTX 3090 is not a compromise — it is a deliberate value choice that makes the right trade-offs for most LLM users. If you find yourself running 34B models regularly, the 5090's 32GB tips the scales. Otherwise, pocket the $1,200 difference.

For more context on used GPU picks, see our best used GPU for LLM guide. If you run through Ollama, our best GPU for Ollama article covers setup and per-model benchmarks. For the current-gen flagship comparison, see RTX 5090 vs 4090 for LLM. Looking at the cheaper Blackwell alternative? Our RTX 5070 Ti vs 3090 for LLM breakdown covers the new $750 vs used $600 decision.

Related guides on Best GPU for LLM

The full version lives on Best GPU for LLM — VRAM calculator, GPU comparison table, and live Amazon pricing.

Best GPU for Whisper in 2026: 6 Cards Speed-Ranked

Thurmon Demich — Tue, 07 Jul 2026 01:15:16 +0000

This article was originally published on Best GPU for AI. The full version with interactive tools, FAQ, and live pricing is on the original site.

You want to transcribe hours of audio locally — interviews, meetings, lectures, podcasts — without uploading sensitive recordings to a cloud API. Whisper runs entirely on your machine, produces accurate transcripts in dozens of languages, and the only bottleneck is your GPU. Here is what you need.

Who this is for

This guide covers GPU selection for running OpenAI Whisper (and faster-whisper / whisper.cpp) locally. Whether you transcribe a few files per week or process hundreds of hours in batch, the right card depends on model size and throughput requirements.

Whisper model VRAM requirements

Model	Parameters	VRAM (FP16)	VRAM (INT8)	Relative Speed
tiny	39M	~1GB	Under 1GB	32x real-time
base	74M	~1GB	Under 1GB	20x real-time
small	244M	~2GB	~1GB	10x real-time
medium	769M	~5GB	~3GB	5x real-time
large-v3	1.5B	~10GB	~5GB	2-3x real-time

Whisper is a lightweight workload compared to LLMs or image generation. Even the largest model fits in 10GB VRAM. The question is not "can my GPU run it" but "how fast."

Transcription speed by GPU

GPU	VRAM	large-v3 (FP16)	large-v3 (INT8)	Price
RTX 5090	32GB	~15x real-time	~20x real-time	~$2,000+
RTX 4090	24GB	~12x real-time	~16x real-time	~$1,600
RTX 4070 Ti Super	16GB	~8x real-time	~11x real-time	~$700
RTX 4060 Ti 16GB	16GB	~5x real-time	~7x real-time	~$400
RTX 4060	8GB	~4x real-time	~6x real-time	~$280
RTX 3060 12GB	12GB	~3x real-time	~4x real-time	~$250 used

"Real-time" means a 1-hour audio file takes 1 hour. At 5x real-time, that same file finishes in 12 minutes. At 12x, it takes 5 minutes.

GPU tier list available at the original article

Best picks by use case

Casual transcription (a few files per week)

RTX 4060 (~$280) — Whisper large-v3 runs at 4-6x real-time. A 1-hour recording finishes in 10-15 minutes. For occasional use, this is more than fast enough. Whisper is one of the few AI workloads where 8GB VRAM is not a limitation.

Regular transcription (daily use, multiple files)

RTX 4060 Ti 16GB (~$400) — Faster compute pushes large-v3 to 5-7x real-time. The extra VRAM means you can run Whisper alongside other applications. Best value for anyone who transcribes regularly.

Batch processing (hundreds of hours)

RTX 4090 (~$1,600) — At 12-16x real-time, you process a full 8-hour workday of recordings in under an hour. The raw throughput makes a real difference when you have backlogs of content to transcribe.

Optimization tips

Use faster-whisper instead of the original OpenAI implementation — it is 2-4x faster with CTranslate2 backend
Use INT8 quantization — minimal accuracy loss with 30-50% faster inference
Batch process by splitting long audio into chunks and processing in parallel
Use VAD (Voice Activity Detection) to skip silence — saves 10-30% processing time on recordings with pauses
Run whisper.cpp for maximum CPU+GPU efficiency on lower-end hardware

Which GPU should you buy?

Transcribing a few files occasionally: The RTX 4060 at $280 handles Whisper large-v3 at 4-6x real-time. Good enough for personal use.

Daily transcription for work or content creation: The RTX 4060 Ti 16GB at $400 is the sweet spot. Reliable speed and enough VRAM for multitasking.

Batch processing large audio archives: The RTX 4090 at $1,600 cuts processing time to minutes per hour of audio. Worth it if transcription is a core part of your workflow.

Whisper is your only AI workload: Do not overbuy. Even a $250 used RTX 3060 runs Whisper large-v3 at 3-4x real-time.

Common mistakes to avoid

Buying a flagship GPU just for Whisper. Whisper is one of the least demanding AI workloads. Unless you also run LLMs or generate images, a mid-range card is more than enough.
Running the tiny or base model to save VRAM. The quality difference between tiny and large-v3 is dramatic, especially for non-English or noisy audio. Use large-v3 with INT8 if VRAM is tight.
Using the original Whisper implementation. Switch to faster-whisper (CTranslate2) for a 2-4x speed improvement with identical accuracy.
Processing long files as a single chunk. Split audio into 30-second segments for better GPU utilization and lower peak VRAM.

Final verdict

Budget	GPU	Whisper Speed
$250	RTX 3060 12GB (used)	3-4x real-time
$280	RTX 4060	4-6x real-time
$400	RTX 4060 Ti 16GB	5-7x real-time
$1,600	RTX 4090	12-16x real-time

Whisper is the rare AI workload where budget GPUs shine. A $280 RTX 4060 transcribes faster than any human typist. For broader AI use beyond transcription, see our general AI GPU guide and best GPUs under $500.

Whisper runs well on almost anything with a GPU. Buy for your other AI workloads first and let Whisper ride along for free.

Related guides on Best GPU for AI

The full version lives on Best GPU for AI — VRAM calculator, GPU comparison table, and live Amazon pricing.