Thurmon Demich

Posted on May 15 • Originally published at bestgpuforllm.com

Best GPU for Llama 70B in 2026 (48GB+ VRAM Required)

#gpu #llama #70b #vram

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: You need at least 48GB of VRAM to run Llama 70B at usable quality. A single RTX 5090 (32GB) can run it at aggressive Q3/Q4 quantization, but for good quality you'll need dual GPUs or a workstation card like the A6000.

The VRAM problem with 70B models

Llama 70B is one of the most capable open-source language models available, but it's demanding. Here's how much VRAM it actually needs:

VRAM chart available at the original article

Quantization	Model Size	VRAM Required	Quality Impact
FP16 (full)	~140GB	140GB+	Best quality
Q8	~70GB	72GB+	Near-lossless
Q6_K	~54GB	56GB+	Minimal loss
Q5_K_M	~48GB	50GB+	Slight loss
Q4_K_M	~40GB	42GB+	Noticeable on complex tasks
Q3_K_M	~32GB	34GB+	Significant degradation
Q2_K	~25GB	28GB+	Major quality loss

The VRAM column includes overhead for context window and KV cache. Actual usage varies with context length.

GPU options for Llama 70B

Single GPU options

GPU	VRAM	Can Run 70B?	Best Quantization	Price
RTX 5090	32GB	Yes, limited	Q3_K_M (degraded)	~$2,000
RTX 4090	24GB	Barely	Q2_K only (poor)	~$1,600
A6000	48GB	Yes	Q4_K_M+ (good)	~$3,500
A100 80GB	80GB	Yes	Q8+ (excellent)	~$8,000+

Dual GPU options

Setup	Total VRAM	Best Quantization	Approx Cost
2x RTX 3090	48GB	Q4_K_M (good)	~$1,800 used
2x RTX 4090	48GB	Q5_K_M (great)	~$3,200
2x RTX 5090	64GB	Q6_K (excellent)	~$4,000+

Best approaches by budget

Budget: Under $2,000 — Dual RTX 3090

The cheapest way to run Llama 70B at decent quality:

48GB combined VRAM handles Q4_K_M quantization
RTX 3090s are widely available used for $800-900 each — see our dual RTX 3090 setup guide for the full build walkthrough
Ollama and llama.cpp support multi-GPU splitting natively
Inference speed is slower due to inter-GPU communication

Downsides: Needs a motherboard with two x16 PCIe slots, a beefy PSU (1200W+), and good case airflow. Two cards at 350W each generate serious heat.

Mid-range: $2,000-4,000 — RTX 5090 or dual 4090

Single RTX 5090: Simplest setup. Can run 70B at Q3_K_M, which is usable but you'll notice quality loss on reasoning-heavy tasks. Best if you also use the GPU for smaller models where it excels. For tips on making the most of a single-card 70B setup, see how to run 70B on a single GPU, and for a broader look at the $2,000 tier our best GPU for LLM under $2,000 guide ranks the alternatives.

Dual RTX 4090: 48GB total VRAM for Q4_K_M+ quality. Better output quality than a single 5090, but more complex setup and higher power draw.

High-end: $3,500+ — NVIDIA A6000

The NVIDIA A6000 with 48GB VRAM on a single card is the cleanest solution:

Runs Q4_K_M and Q5_K_M on one card
No multi-GPU complexity
Professional-grade reliability
ECC memory for consistent results

The downside is price and availability. The A6000 is a professional card with professional pricing.

Ollama setup for multi-GPU

If you go the dual-GPU route, Ollama handles GPU splitting automatically:

OLLAMA_NUM_GPU=999 ollama run llama3:70b-q4_K_M

For llama.cpp, specify the split:

--tensor-split 24,24

Both tools will distribute model layers across available GPUs. Inference speed scales roughly 60-70% of linear with two cards due to communication overhead.

Inference speed expectations

Setup	Llama 70B Q4_K_M	Tokens/sec
Single A6000 (48GB)	Full model on GPU	~15-20 tok/s
2x RTX 4090 (48GB)	Split across GPUs	~12-18 tok/s
2x RTX 3090 (48GB)	Split across GPUs	~8-12 tok/s
Single RTX 5090 (Q3)	Degraded quality	~18-22 tok/s
CPU offload (partial)	Slow	~2-5 tok/s

These are approximate for 2048 context length. Longer contexts reduce speed.

Should you even run 70B locally?

Before investing in hardware, consider:

Is 70B actually better for your use case? For many tasks, a well-prompted 13B or fine-tuned 34B model performs nearly as well.
Would cloud be cheaper? If you only need 70B occasionally, cloud GPU rental (RunPod, Vast.ai) at $1-2/hour may be more cost-effective than a $3,000+ hardware investment. See RunPod vs Vast.ai for LLM to understand which platform offers better pricing and reliability for this workload, and our cloud GPU TCO vs self-hosted LLM breakdown for the exact monthly break-even math.
Do you need the privacy? Local inference means your data never leaves your machine. If that matters, the hardware cost is justified.

Which GPU should YOU buy for Llama 70B?

Running 70B as your primary model? Get 2x RTX 4090 ($3,200). 48GB combined VRAM handles Q4_K_M with good quality and decent speed.
Running 70B occasionally alongside smaller models? Get an RTX 5090 ($2,000). Handles Q3_K_M for 70B and excels at 7B-34B models the rest of the time.
Need the best single-card 70B experience? Get an NVIDIA A6000 ($3,500). 48GB on one card means Q4_K_M+ without multi-GPU complexity.
Only need 70B sometimes? Use cloud GPUs instead. $1-2/hour beats a $3,000+ hardware investment for occasional use.

Common mistakes to avoid

Buying a single 24GB GPU expecting to run 70B — the RTX 4090 at 24GB can only fit Q2_K quantization, where output quality is significantly degraded. You need 32GB minimum, and realistically 48GB for good results.
Ignoring memory bandwidth in dual-GPU setups — inter-GPU communication adds latency. Two RTX 3090s (936 GB/s each) outperform two RTX 4060 Tis even if total VRAM is similar, because bandwidth determines token generation speed.
Not accounting for context length VRAM overhead — at Q4_K_M, Llama 70B uses ~40GB for weights alone. A 4K context window adds 3-5GB for the KV cache. Plan your VRAM budget accordingly. For a full breakdown of exactly how much VRAM each 70B quantization level needs, see how much VRAM for a 70B model.
Skipping the "do I actually need 70B" question — a well-quantized 34B model on a single RTX 4090 often matches 70B at Q2_K in output quality, at 3x the inference speed and half the hardware cost. Llama 4 Scout is another alternative worth considering — it beats Llama 3 70B on benchmarks and fits on a single RTX 5090; see our Llama 4 Scout GPU guide for details. DeepSeek's reasoning-tuned 32B is another single-card alternative — see our DeepSeek GPU guide for VRAM needs and tok/s on 24GB cards. If you are wondering whether a budget card like the 4060 Ti can even attempt 70B, see can the RTX 4060 Ti run Llama 70B?

Final verdict

Situation	Recommendation
Must be single GPU	NVIDIA A6000 (48GB)
Best value	2x RTX 3090 used (~$1,800)
Best performance/value	2x RTX 4090 (~$3,200)
Occasional 70B use	Cloud GPU (RunPod/Vast.ai)
Mostly smaller models	RTX 5090 single card

For most people, Llama 70B is not a single-GPU workload at consumer prices. Accept that and plan for either dual GPUs, a workstation card, or cloud.

The best GPU for Llama 70B is the one that gives you enough VRAM to avoid aggressive quantization. Quality degrades fast below Q4 — don't sacrifice output quality to save on hardware.

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