This article was originally published on runaihome.com
The three names that come up when you ask about running frontier-class LLMs at home are Llama 3.3, Qwen3, and Mistral Large. Two of them are legitimate choices for a home lab in 2026. The third one is a trap.
Mistral Large 2 is a 123B-parameter model that Mistral AI has never released as open weights. It's a closed commercial API — you pay per token through their platform. Even if you somehow got the weights, running 123B parameters at Q4_K_M quantization requires approximately 73 GB of VRAM, meaning four RTX 4090s just to fit the model. On consumer hardware, it's not a local AI story. It's a cloud AI story with a local AI brand name.
The real comparison for home lab users is: Llama 3.3 70B, Qwen3 32B, and Mistral Small 24B. Three open-weight models, three different hardware tiers, and meaningfully different performance profiles. Here's the data to make the call.
The Contenders
Llama 3.3 70B (Meta, December 2024)
Meta's Llama 3.3 70B Instruct is the gold standard for open-weight models at the 70B tier. Released December 6, 2024, it matches or beats Llama 3.1 405B on several key benchmarks while being roughly five times cheaper to run. The license is Meta's Llama 3.3 Community License — commercial use is permitted for most businesses (there's a restriction for platforms with over 700 million monthly active users, which isn't you).
Specs: 70B parameters, 128K context window, text-only.
Benchmarks (official Meta):
- MMLU: 86.0%
- HumanEval: 88.4%
- GPQA Diamond: 50.5%
- MATH: 77.0%
- IFEval: 92.1%
The hardware problem: At Q4_K_M quantization, Llama 3.3 70B weighs approximately 42 GB. That's more than a single RTX 4090 (24 GB) or RTX 5060 Ti (16 GB) can hold. A single 24 GB GPU has to offload layers to system RAM, dropping throughput to 8–15 tokens per second — slow enough that the experience feels like watching paint dry. To run Llama 3.3 70B at useful speeds, you need either dual 24 GB GPUs, a Mac with 64 GB+ unified memory, or a workstation with high-VRAM cards like an NVIDIA A100 or H100.
Qwen3 32B (Alibaba Cloud, April 2025)
Qwen3 32B is the model that changed the conversation about 30B-tier efficiency. Released April 28, 2025, it runs under Apache 2.0 — fully open, no usage restrictions, commercial deployment without asking permission from Alibaba. The dense 32B model (not to be confused with Qwen3's MoE variants) fits on a single RTX 4090 at Q4_K_M quantization using approximately 19–22 GB VRAM.
Specs: 32B dense parameters, 32K native context window, text-only.
Benchmarks (Qwen3 Technical Report, May 2025):
- MMLU-Pro: 65.54 (base model — instruct improves on this)
- Qwen3-32B-Base outperforms Qwen2.5-72B-Base across most tasks
The killer feature: Qwen3 32B ships with a toggleable thinking mode. When you prepend /think to a prompt (or set enable_thinking=True), the model reasons step-by-step before answering — similar to DeepSeek R1 behavior. /no_think reverts to instant responses. The same model handles both modes, giving you a reasoning model and a fast chat model in one 19 GB download.
The single-GPU caveat: At Q4_K_M, Qwen3 32B uses 19–22 GB on a 24 GB card, leaving 2–5 GB for KV cache. That's enough for conversations up to a few thousand tokens but starts to constrain long-document work. If you're planning to feed it 20-page PDFs or long code files, budget for a Q3 quantization to recover headroom, or step down to Qwen3 14B (8.3 GB at Q4).
Mistral Small 3.1 24B (Mistral AI, March 2025)
Mistral's locally-viable entry in this tier is not Mistral Large — it's Mistral Small 3.1 (and the updated 3.2 variant released June 2025). At 24B parameters, it runs at approximately 13.4 GB at Q4_K_M, comfortably fitting on a single RTX 3060 12 GB with light quantization or an RTX 4090 with substantial VRAM headroom for long contexts. Apache 2.0 license.
Specs: 24B parameters, 128K context window, multimodal (vision input supported).
Benchmarks (Mistral AI official, March 2025):
- MMLU: 81.0%
- HumanEval: 88.4%
- GPQA: 37.5%
- MATH: 69.3%
The speed advantage: On an RTX 4090 at Q4_K_M, Mistral Small 3.1 runs at approximately 55 tokens per second. That's over 3x faster than Llama 3.3 70B on the same hardware. The quality trade-off is real (MMLU 81% vs. 86%) but the responsiveness difference is what you feel during a long coding session or multi-turn conversation.
Hardware Requirements at a Glance
| Model | Q4_K_M VRAM | Min GPU | Comfortable GPU | Tok/s (RTX 4090) |
|---|---|---|---|---|
| Mistral Small 3.1 24B | ~13.4 GB | RTX 3060 12 GB (Q3) | RTX 4070 Ti 16 GB | ~55 |
| Qwen3 32B | ~19–22 GB | RTX 4090 24 GB | 2× RTX 3090 | ~30–35 |
| Llama 3.3 70B | ~42 GB | 2× RTX 4090 | Mac Studio M3 Ultra (192 GB) | ~8–15 (w/ offload) |
Tokens per second for Llama 3.3 70B on a single RTX 4090 are measured with partial CPU offloading. On dual 24 GB GPUs (full VRAM fit), expect 18–27 tok/s depending on generation length.
If you're shopping for the GPU to run any of these, the GPU buying guide for local AI and the RTX 5060 Ti vs RTX 4060 Ti comparison cover the current consumer landscape. For AMD users: ROCm 7.2 makes Llama 3.3 70B and Qwen3 32B fully functional on Linux with RX 7900 XTX-class hardware — full details in AMD ROCm in 2026: Is It Finally Usable?
Use-Case Decision Matrix
| Use Case | Winner | Why |
|---|---|---|
| Chat assistant (single user, fast responses) | Mistral Small 3.1 24B | 55 tok/s feels instant; 81% MMLU handles most queries; 128K context for long docs |
| Coding — code generation and completion | Qwen3 32B | Ties Llama 3.3 on HumanEval; thinking mode handles hard algorithmic problems; fits single 4090 |
| Reasoning / math / complex problem solving | Llama 3.3 70B | 50.5% GPQA Diamond and 77% MATH are the widest margins; needs dual GPUs |
| Local image + text workflows | Mistral Small 3.1 24B | Only one of the three with native vision input; handles multimodal pipelines that the others can't |
| Multilingual use (non-English primary) | Qwen3 32B | Alibaba trained Qwen3 on a significantly broader multilingual corpus than Meta's Llama 3 |
| Running on 16 GB GPU (RTX 5060 Ti, 4060 Ti) | Mistral Small 3.1 24B | Only viable Q4 option at 13.4 GB; Qwen3 32B doesn't fit without dropping to Q2 |
| Budget Mac (M2/M3 base, 16–36 GB RAM) | Qwen3 32B | 32B Q4 runs well on 36 GB unified memory; Llama 3.3 70B is borderline at 36 GB |
| Server inference (multi-user, vLLM/Ollama) | Llama 3.3 70B | Higher absolute quality ceiling matters more when multiple users submit varied tasks |
Quality vs. Speed: The Real Trade-Off
At the 70B vs 32B vs 24B tier, the benchmark gaps are smaller than they look on paper. The 5-point MMLU difference between Llama 3.3 70B (86%) and Mistral Small 24B (81%) rarely matters for most chat or coding work. What matters in daily use is tokens per second.
55 tok/s (Mistral Small) means a 300-word answer arrives in about 10 seconds. 8–15 tok/s (Llama 3.3 offloaded) means the same answer takes 45–90 seconds. Over a 2-hour session, the difference between a snappy assistant and a slow one is the difference between staying in flow and watching your terminal.
The cases where Llama 3.3 70B's quality ceiling earns its hardw
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