Mistral Small 24B vs Qwen 3 14B vs Llama 3.3 8B: Which to Run Locally?
Quick Answer
Pick by VRAM: Llama 3.3 8B (4.9 GB), Qwen 3 14B (9.3 GB), Mistral Small 3.1 24B (14.4 GB). Qwen 14B wins at 12 GB VRAM. Mistral Small 24B wins above 16 GB on reasoning tasks.
- ▸Llama 3.3 8B Q4_K_M: 4.9 GB VRAM, ~45 tok/s on RTX 4090, MMLU 66.6% — best for 6–8 GB cards
- ▸Qwen 3 14B Q4_K_M: 9.3 GB VRAM, ~28 tok/s, MMLU 74.8% — sweet spot for 12 GB cards
- ▸Mistral Small 3.1 24B Q4_K_M: 14.4 GB VRAM, ~20 tok/s, MMLU ~81% — only for 16 GB+ cards
Updated: June 20, 2026
Key Takeaways
- ✓Llama 3.3 8B at Q4_K_M uses 4.9 GB VRAM and runs at ~45 tok/s on RTX 4090 — the only viable model in this group for 6 GB cards
- ✓Qwen 3 14B at Q4_K_M uses 9.3 GB and scores 74.8% MMLU — the sweet spot for 12 GB cards like the RTX 3060 12 GB or RTX 4060 Ti 16 GB
- ✓Mistral Small 3.1 24B at Q4_K_M uses 14.4 GB and reaches ~81% MMLU — only feasible on 16 GB cards (RTX 4080, RTX 3090, RTX 4090)
- ✓For coding on 12 GB: Qwen 3 Coder 14B. For multilingual reasoning on 16 GB+: Mistral Small 3.1 24B. Below 10 GB: Llama 3.3 8B.
VRAM Requirements: Which Card Runs Which Model
The choice between these three models is primarily a VRAM decision. At Q4_K_M quantization: Llama 3.3 8B uses 4.9 GB, Qwen 3 14B uses 9.3 GB, and Mistral Small 3.1 24B uses 14.4 GB. This maps directly onto three GPU tiers: 6–8 GB cards (Llama 3.3 8B only), 10–12 GB cards (Qwen 3 14B), and 16+ GB cards (Mistral Small 24B).
Speed on RTX 4090 at Q4_K_M: Llama 3.3 8B runs at approximately 45 tok/s, Qwen 3 14B at ~28 tok/s, and Mistral Small 3.1 24B at ~20 tok/s. On an RTX 3060 12 GB, only Llama 3.3 8B and Qwen 3 14B fit — Mistral Small 24B requires at minimum a 16 GB card to avoid spilling to CPU RAM.
The benchmark spread is meaningful: Mistral Small 24B's 81% MMLU is 14 points above Llama 3.3 8B and 6 points above Qwen 3 14B. On complex multi-step reasoning and instruction-following tasks, this gap is noticeable in practice.
| Model | VRAM (Q4_K_M) | Speed (RTX 4090) | MMLU | Minimum GPU |
|---|---|---|---|---|
| Llama 3.3 8B | 4.9 GB | ~45 tok/s | 66.6% | RTX 3060 6 GB |
| Qwen 3 14B | 9.3 GB | ~28 tok/s | 74.8% | RTX 3060 12 GB |
| Mistral Small 3.1 24B | 14.4 GB | ~20 tok/s | ~81% | RTX 4080 16 GB |
Quality vs VRAM: When Each Model Wins
Llama 3.3 8B wins on VRAM efficiency. At 4.9 GB Q4_K_M it is the only model in this group that fits a 6 GB card with headroom for a 4k token context window. It scores 66.6% on MMLU and delivers snappy interactive responses (~45 tok/s on RTX 4090). For chat, quick coding queries, and daily use on constrained hardware, it is the correct pick.
Qwen 3 14B wins at 12 GB VRAM. Its 74.8% MMLU places it well above Llama 3.3 8B on reasoning and coding — and it fits within the most common prosumer GPU tier. The Qwen Coder 14B variant (same size, code-tuned) scores approximately 78% on HumanEval. If your primary use is coding and you have a 12 GB card, Qwen 3 14B is the answer.
Mistral Small 3.1 24B wins on quality when VRAM allows. Its 81% MMLU and strong multilingual performance make it the top choice for 16 GB cards. It handles long-form reasoning, structured output tasks, and complex instruction sets more reliably than the 14B-class models. On an RTX 4090 24 GB it fits at Q5_K_M for even better quality.
For a direct 14B-class comparison see the Qwen 14B vs Llama 8B comparison, which includes coding benchmark detail.
Quick Answers: Mistral Small 24B vs Qwen 14B vs Llama 8B
Can Mistral Small 24B run on an RTX 3060 12 GB?▾
Is Mistral Small 24B better than Qwen 3 14B for coding?▾
Which model should I use on a 16 GB GPU like the RTX 4080?▾
How does Llama 3.3 8B compare to Llama 3.2?▾
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