Code
Devstral
Devstral is a Mistral-family model worth evaluating for developer and coding workflow evaluation.
Mistral AI · Mistral
Editorial review
Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.
Best for
Developer and coding workflow evaluation
Who should use it
- Developer and coding workflow evaluation
- Builders who want local or self-hosted testing options.
Common workflows
- Developer and coding workflow evaluation
- code workflows
- local workflows
- open weights workflows
Deployment and hardware notes
Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
License and usage notes
Apache 2.0. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.
Strengths
- Open weights where released model option for Mistral workflows.
- Developer and coding workflow evaluation
- Use the exact checkpoint and quantization that matches your hardware and latency target.
Limitations
- Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
- Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
- Context window and limits: 131,072 tokens.
- Verify the exact model card, provider docs, license, and serving support before production use.
Local workflow notes
Use the exact checkpoint and quantization that matches your hardware and latency target.
Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers
Platforms: Windows, macOS, Linux
Will Devstral run on your machine?
Devstral is 23.6B parameters and needs 15.8 GB of VRAM at Q4_K_M — 14.3 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.
VRAM by quantization
| Quantization | Weights | Needs (with overhead) | Quality |
|---|---|---|---|
| Q4_K_M | 14.3 GB | 15.8 GB | good |
| Q8_0 | 25 GB | 26.5 GB | high |
| FP16 | 47 GB | 48.5 GB | reference |
Fit on common hardware at Q4_K_M
| Hardware | Memory the model can use | System RAM | Verdict |
|---|---|---|---|
| CPU Only | None (CPU only) | 16 GB | Too large |
| RTX 4060 Laptop | 8 GB | 16 GB | Too large |
| RTX 3060 (12GB) | 12 GB | 32 GB | CPU offload |
| RTX 4060 Ti (16GB) | 16 GB | 32 GB | Tight |
| RTX 3090 | 24 GB | 64 GB | Comfortable |
| Apple Silicon (Unified Memory) 36 GB | 27 GB of 36 GB | 36 GB | Comfortable |
| RTX 5090 | 32 GB | 64 GB | Comfortable |
Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.
Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.
Need more hardware for Devstral? Open the PC Builder for the 30B / 32B tier →
VRAM fit by quantization level
Enter your GPU VRAM below to see which quantization of Devstral fits and get the Ollama run command.
Sources to verify
Related resources
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