Mistral Small 3.2 24B: VRAM Requirements & Local Setup Guide 2026

Mistral AI's efficient 24B multimodal model. Standout HumanEval score (92.9%) makes it one of the top open coding models in its size class.

Provider: Mistral AI
Architecture: Dense Transformer
Total Parameters: 24B
Active Params/Token: Unknown
Context Window: 131K tokens
License: open
Modalities: text, image
HuggingFace: mistralai/Mistral-Small-3.2-24B-Instruct-2506

VRAM Requirements by Quantization & Context Length

All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.

Quantization	4K ctx	8K ctx	32K ctx	128K ctx
Q4_K_M	15.3 GB	15.9 GB	19.6 GB	34.6 GB
Q5_K_M	17.6 GB	18.2 GB	21.9 GB	36.9 GB
Q6_K	20.0 GB	20.6 GB	24.4 GB	39.4 GB
Q8_0	25.4 GB	26.0 GB	29.7 GB	44.7 GB
FP16	46.3 GB	47.0 GB	50.7 GB	65.7 GB

Weights = 24B params × GGUF bits per weight (Q4_K_M ≈ 4.9); plus runtime overhead; plus fp16 KV cache from config.json: 8 KV heads × 128 dims — ~160 KB per token at short context. Same engine as the Can I Run LLM calculator.

Best GPU for Mistral Small 3.2 24B by Budget

Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.

Under $500 (Entry GPU) Not feasible at this budget
$500–$1,000 (Mid-Range GPU) RTX 3090 24GB (tight)
$1,000–$3,000 (High-End GPU) RTX 4090 (24GB)
$3,000+ (Workstation / Cloud) A100 40GB

Benchmark Scores

Scores reported by Mistral AI or verified third-party evaluations. Higher is better for all benchmarks except where noted.

Benchmark	Score
MMLU-Pro	69.06
GPQA Diamond	46.13
HumanEval	92.9
IFEval	84.78
MMMU	62.5

How to Run Mistral Small 3.2 24B Locally

Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.

Option A — Ollama (Recommended for Beginners)

Ollama is the easiest way to run Mistral Small 3.2 24B locally. It handles model downloading, GGUF quantization selection, and serving automatically.

Install Ollama from ollama.com
Pull the model:
```
ollama pull mistral-small3.2:24b
```
Run interactively:
```
ollama run mistral-small3.2:24b
```

Option B — HuggingFace Hub

For more control over quantization format, download directly from HuggingFace:

pip install huggingface_hub
huggingface-cli download mistralai/Mistral-Small-3.2-24B-Instruct-2506

Hardware note: Check the VRAM table above before downloading. Running Mistral Small 3.2 24B requires substantial GPU memory — use Q4_K_M quantization for the lowest VRAM footprint while retaining most model quality.

Strengths and Use Cases

Best coding performance per GB of VRAM in its class (HumanEval 92.9%). Multimodal with vision support. Fits on a single RTX 4090 at Q4_K_M.

When choosing a local LLM, Mistral Small 3.2 24B is worth considering if your workload aligns with its design goals. As a Dense Transformer architecture from Mistral AI, it offers a distinct trade-off between compute efficiency and capability. Its dense architecture provides consistent, predictable performance across a wide range of tasks. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.

For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.

Ready to Check Your Hardware?

Use our interactive calculator to see exactly whether your GPU can run Mistral Small 3.2 24B — and at what quantization level.

Check if your GPU can run this model → Compare on LLM Leaderboard

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