Magistral 32B
Mistral's reasoning-specialized fine-tune of a Mistral Small base. Reasoning-token emission similar to Qwen 3 / DeepSeek R1 in a smaller footprint. Research license — non-commercial use is open.
Positioning
Magistral 32B is a dense 32-billion-parameter model from Mistral AI, released under the Mistral Research License. It is a reasoning-specialized fine-tune of the Mistral Small base, designed to emit reasoning tokens in a style similar to other reasoning-focused models but in a smaller, dense footprint. With a 131,072-token context window, it targets research and non-commercial use cases that require extended reasoning chains. Its dense architecture means all 32B parameters are active during inference, placing it in the workstation deployment class.
Strengths
- Reasoning-specialized fine-tune: Built on Mistral Small with a focus on chain-of-thought reasoning, making it suitable for complex logical tasks without the overhead of larger models.
- Large context window: 131,072 tokens of context allow processing of long documents, multi-turn conversations, or extended reasoning traces.
- Dense architecture simplicity: Unlike mixture-of-experts models, all 32B parameters are always active, which can simplify deployment and provide predictable memory usage.
- Permissive research license: The Mistral Research License allows open non-commercial use, making it accessible for academic and personal research projects.
Limitations
- Non-commercial license only: Commercial deployment is not permitted under the Mistral Research License, limiting its use in production or revenue-generating applications.
- High memory requirements: At FP16, the model requires 64 GB of disk space, and even at Q4_K_M (18 GB), the full 131K context can demand significant additional memory for KV cache and framework overhead (30-50% extra).
- No community benchmarks available: We do not yet have independent, community-reported benchmark results for this model. Published vendor metrics should be treated as best-case until verified by third parties.
- Dense 32B parameter cost: Unlike MoE models that activate only a fraction of parameters per token, Magistral 32B uses all 32B parameters for every forward pass, meaning inference compute cost is proportional to a full 32B-parameter dense model.
What it takes to run this locally
Magistral 32B requires a workstation-class setup. Quantized sizes range from ~64 GB (FP16) down to ~10.4 GB (Q2_K). For practical use with the full 131K context, add 30-50% overhead for KV cache and framework memory. A single GPU with 48 GB VRAM (e.g., RTX 6000 Ada, A6000) can run Q4_K_M or Q3_K_M with moderate context lengths. Dual 24 GB GPUs (e.g., two RTX 4090s) can also handle Q4_K_M via tensor parallelism. For full FP16 precision, multiple A100s or similar datacenter hardware are needed.
Should you run this locally?
Yes if you are conducting non-commercial research into reasoning models and need a dense 32B-parameter model with a large context window, and you have access to workstation-class GPUs (48 GB VRAM or dual 24 GB). The Mistral Research License makes it easy to experiment without licensing fees.
No if you need commercial deployment rights, or if your hardware is limited to consumer GPUs with 12-24 GB VRAM — even the smallest quant (Q2_K) may struggle with the full context length. Also, if you prefer an MoE architecture for lower per-token compute, consider other models.
Catalog cross-links
- Mistral Small
- Mistral Research License
- Workstation deployment guide
Overview
Mistral's reasoning-specialized fine-tune of a Mistral Small base. Reasoning-token emission similar to Qwen 3 / DeepSeek R1 in a smaller footprint. Research license — non-commercial use is open.
How to run it
Magistral 32B is Mistral AI's 32B dense model — a mid-tier entry in the Mistral family optimized for efficiency and quality at manageable size. Run at Q4_K_M via Ollama (ollama pull magistral:32b) or llama.cpp with -ngl 999 -fa -c 8192. Q4_K_M file size ~18 GB on disk. Minimum VRAM: 16 GB — RTX 4080 (16GB) at Q4_K_M with KV offload for 4K context. RTX 4090 24GB: Q4_K_M comfortably at 16K context. Recommended: RTX 4090 24GB at Q4_K_M. Throughput: ~35-55 tok/s on RTX 4090 at Q4_K_M. Mistral architecture — well-supported. Magistral is positioned as Mistral's efficient general-purpose model: strong multilingual, good coding, competitive reasoning. The 32B class is the efficiency sweet spot — 70B-class quality impression at half the VRAM. Use for: multilingual chat, coding, general reasoning, agent tasks. For larger Mistral models: Mistral Large 2 (123B) or Mistral Medium 3.5. For smaller: Mistral Small 3.2 24B. Context: 32K+ advertised; practical at Q4 on 24 GB is 16-32K.
Hardware guidance
Minimum: RTX 3060 12GB at Q3_K_M with KV offload. Recommended: RTX 4090 24GB at Q4_K_M (16K context). Optimal: RTX 5090 32GB at Q4_K_M (32K+ context). VRAM math: 32B dense, Q4_K_M ≈ 18 GB. KV cache at 16K: ~8 GB. Total: ~26 GB at 16K. RTX 4090 24GB: Q4 + 8-12K context fits on-GPU. 16K context: offload KV. RTX 3090 24GB: same. RTX 4080 16GB: Q4 + 2K on-GPU. MacBook Pro M4 Pro 24GB+: Q4 at 10-20 tok/s. Cloud: A10 24GB at Q4_K_M. AWQ-INT4 drops weights to ~16 GB — 16K context fits on 24 GB on-GPU. Magistral's 32B size is one of the most hardware-efficient ways to get 70B-class quality.
What breaks first
- Mistral tokenizer quirks. Mistral's tokenizer handles whitespace and code indentation differently from Llama. Python code formatted with mixed tabs/spaces may produce unexpected token counts. 2. Magistral vs Mistral Small/Medium naming. Mistral's naming convention (Magistral, Small, Medium, Large) maps to size tiers. Magistral 32B is between Small (24B) and Medium (123B). Don't confuse the models. 3. Multilingual variance. Magistral's multilingual quality varies significantly by language. Indo-European languages are strong; others may be weaker. Benchmark your target language. 4. Tool-calling format. Magistral's function-calling format may differ from OpenAI's standard. Test the exact JSON schema your app expects.
Runtime recommendation
Common beginner mistakes
Mistake: Confusing Magistral with Mistral Small or Medium. Fix: Magistral is a distinct 32B model in Mistral's lineup. Check the hf repo for the specific model name and verify size. Mistake: Using Llama chat template with Magistral. Fix: Mistral models use Mistral-specific chat templates. Verify on hf tokenizer_config.json. Mistake: Pulling ollama pull mistral:32b and expecting Magistral. Fix: The Ollama tag may be magistral:latest or different from mistral:32b. Check Ollama's catalog. Mistake: Underestimating multilingual quality variance. Fix: Magistral's quality drops for languages outside its training distribution. Test your specific language thoroughly before deploying.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Reasoning-class quality at 32B
- Mistral instruction-following lineage
Weaknesses
- Research license blocks commercial use
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| AWQ-INT4 | 19.0 GB | 22 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Magistral 32B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Magistral 32B?
Can I use Magistral 32B commercially?
What's the context length of Magistral 32B?
Source: huggingface.co/mistralai/Magistral-32B
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Magistral 32B runs on your specific hardware before committing money.