▸Mistral Small 3.1 24B Q4_K_M: 14.4 GB VRAM, ~20 tok/s, MMLU ~81% — only for 16 GB+ cards
Updated: 2026-05
Model ComparisonsIntermediate
Key Takeaways
✓Llama 3.1 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 2.5 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 2.5 Coder 14B. For multilingual reasoning on 16 GB+: Mistral Small 3.1 24B. Below 10 GB: Llama 3.1 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 3.1 8B uses 4.9 GB, Qwen 2.5 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 3.1 8B only), 10–12 GB cards (Qwen 2.5 14B), and 16+ GB cards (Mistral Small 24B).
Speed on RTX 4090 at Q4_K_M: Llama 3.1 8B runs at approximately 45 tok/s, Qwen 2.5 14B at ~28 tok/s, and Mistral Small 3.1 24B at ~20 tok/s. On an RTX 3060 12 GB, only Llama 3.1 8B and Qwen 2.5 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 3.1 8B and 6 points above Qwen 2.5 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 3.1 8B
4.9 GB
~45 tok/s
66.6%
RTX 3060 6 GB
Qwen 2.5 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 3.1 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 2.5 14B wins at 12 GB VRAM. Its 74.8% MMLU places it well above Llama 3.1 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 2.5 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?▾
No. Mistral Small 3.1 24B at Q4_K_M requires 14.4 GB VRAM, exceeding the RTX 3060 12 GB. Dropping to Q2_K brings it to approximately 7.6 GB but causes significant quality degradation. For RTX 3060 12 GB, Qwen 2.5 14B Q4_K_M (9.3 GB) is the correct choice — it leaves 2.7 GB headroom for context.
Is Mistral Small 24B better than Qwen 2.5 14B for coding?▾
For general coding, Mistral Small 24B has a slight edge due to its larger size. However, Qwen 2.5 Coder 14B (the code-tuned Qwen variant) is competitive with Mistral Small 24B on HumanEval and fits in 12 GB VRAM. If your budget is a 16 GB card and you need both reasoning and coding, Mistral Small 24B wins. On 12 GB, Qwen Coder 14B is the better tradeoff.
Which model should I use on a 16 GB GPU like the RTX 4080?▾
Mistral Small 3.1 24B Q4_K_M at 14.4 GB fits with 1.6 GB headroom — enough for a 2k context window. It outperforms Qwen 2.5 14B on reasoning benchmarks. Alternatively, Qwen 2.5 32B at Q3_K_M fits in approximately 13.5 GB and competes with Mistral Small 24B on coding tasks while offering more parameters.
How does Llama 3.1 8B compare to Llama 3.2?▾
Llama 3.2 8B was not released — the 3.2 series introduced 1B, 3B, and multimodal 11B/90B variants only. Llama 3.1 8B remains the standard 8B Llama reference model. For text-only use at 6–8 GB VRAM, Llama 3.1 8B is the current recommended pick in this size class.
