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mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 19.3 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 23 GB of VRAM.
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | — |
Q3_K_S | 3 | 11.8 GB | Low | — |
NVFP4 | 4 | 13.4 GB | Medium | — |
Q4_K_M | 4 | 14.6 GB | Medium | — |
Q5_K_M | 5 | 17.3 GB | High | — |
Q6_K | 6 | 19.7 GB | High | — |
Q8_0 | 8 | 25.7 GB | Very High | — |
F16 | 16 | 49.2 GB | Maximum | — |
Hardware compatibility
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Memory breakdown
Frequently asked questions
mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 19.3 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc Pro B60 24GB can run mistral small 3.1 24b instruct 2503 hf with a compatibility score of 48/100. It provides 24 GB of memory and achieves approximately 16.8 tokens per second.
The recommended quantization for mistral small 3.1 24b instruct 2503 hf is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for mistral small 3.1 24b instruct 2503 hf: RTX 5090 32GB (score: 56/100), RTX PRO 4500 Blackwell 32GB (score: 55/100), AMD Instinct MI100 32GB (score: 55/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, mistral small 3.1 24b instruct 2503 hf is well-suited for chat. It was designed with these use cases in mind.
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