Best GPU for Mistral Small 3.1 24B Locally
Real-time prices and hardware recommendations updated for August 2026.
Mistral Small 3.1 24B is a dense model: all 24B parameters activate on every token, so generation speed is bound by how fast your card can stream the full weights.
To run Mistral Small 3.1 24B locally you need roughly 26.1 GB of VRAM at Q4_K_M quantization with a 128k token context. The best-value card that fits is the RTX PRO 4500 Blackwell (32 GB), which should generate around 11 tokens per second.
Adjust Context Length
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Recommended Hardware
The 128k context window generates ~12GB of KV cache. Combined with Q3 weights (~10GB), 24GB is the minimum practical entry point.
Q4 weights (~14GB) plus 128k KV cache exceed 24GB. 32GB cards allow comfortable inference at the full context window.
Recommended Hardware
Full Q8 precision at 128k context requires ~40GB. 48GB workstation cards deliver uncompromised quality at the full context window.
Other models with the same VRAM requirement
Because Mistral Small 3.1 24B's weights fit a 32 GB card, other models of a similar size run on the same GPU. Generation speed varies — mixture-of-experts models are faster, dense models slower — but any of these load in the same VRAM:
Optimizing Setup for Mistral Small 3.1 24B
Quantization Recommendations
For daily coding and reasoning tasks, Q4_K_M (4-bit quantization) offers the best balance of quality and memory efficiency — it reduces memory requirements by over 70% with minimal quality loss compared to FP16. Q8 and higher presets preserve more fidelity at the cost of significantly higher VRAM usage, which may force layer offloading and hurt throughput.
Recommended Local Software
We recommend using Ollama as the primary runner for local inference due to its automated GPU model splitting and context cache optimizations. For advanced fine-tuning or quantization splits, llama.cpp with Flash Attention compiled natively provides the best granular control.
Running Mistral Small 3.1 24B locally — FAQ
How much VRAM do I need to run Mistral Small 3.1 24B?
At a 128k context with KV cache quantization on, Mistral Small 3.1 24B needs about 26.1 GB of VRAM at Q4_K_M — the quantization most people should use. Dropping to Q3_K_M brings that down to roughly 22.6 GB at some quality cost, while Q8_0 needs about 39.9 GB for the best quality this model can give.
What size graphics card does Mistral Small 3.1 24B fit on?
Mistral Small 3.1 24B needs about 26.1 GB at Q4_K_M, which is more than a single 24 GB consumer card provides. You need a workstation card, a multi-GPU setup, or a more aggressive quantization — otherwise layers spill into system RAM and generation slows dramatically.
Which quantization should I use for Mistral Small 3.1 24B?
Use Q4_K_M unless you have VRAM to spare. It needs about 26.1 GB and loses very little quality against full precision. Q8_0 needs about 39.9 GB for a quality gain most people cannot detect in everyday coding and chat. Spend spare VRAM on a longer context instead.
How does context length affect the VRAM Mistral Small 3.1 24B needs?
Model weights are fixed, but the KV cache grows linearly with context. For Mistral Small 3.1 24B at a 128k context the cache is about 12.3 GB; doubling to 256k takes it to roughly 24.6 GB. Turning KV cache quantization off doubles those figures again.
Can the same GPU run other models similar to Mistral Small 3.1 24B?
Yes. Mistral Small 3.1 24B needs about 26.1 GB at Q4_K_M, and any model whose weights fit the same card runs on it — including Codestral 22B (22.2B), Qwen3.5-27B (27B), Gemma 2 27B (27.2B), Gemma 3 27B (27.4B). The weights fit the same GPU; generation speed varies (mixture-of-experts models are faster, dense models slower).
▸How token speeds are estimated
Two metrics are shown per GPU: Read tok/s (how fast the model ingests your prompt) and Decode tok/s (how fast it streams tokens back). They model fundamentally different bottlenecks.
Read (Prefill)
The prompt is processed in one parallel pass. This is compute-bound: it saturates the GPU's tensor cores.
Decode (Generation)
Each new token requires loading the entire model's active weights from VRAM. This is memory-bandwidth-bound: the GPU stalls waiting for data, not computing.
Weights = (activeParams × bits ÷ 8) × 1.15 overhead. KV cache per step = activeParams × multiplier × contextK.
Architecture utilization factors
Left: decode factor — Right: read factor
Data sources
TFLOPS and memory bandwidth are read from the GPU database. When missing, bandwidth falls back to a hardcoded dictionary.
Limitations
These are analytical estimates, not benchmark results. Use them as a relative comparison, not an absolute performance guarantee.