Edge

Mistral Research License (non-commercial)Open weights where releasedUpdated August 2026

Ministral 8B

Ministral 8B is a Mistral-family model worth evaluating for efficient local workflows.

Mistral AI · Mistral

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesExact model card

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

Efficient local workflows

Who should use it

Efficient local workflows
Builders who want local or self-hosted testing options.

Common workflows

Efficient local workflows
edge 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

Mistral Research License (non-commercial). 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.
Efficient local workflows
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: 32,768 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 Ministral 8B run on your machine?

Ministral 8B is 8.02B parameters and needs 6.7 GB of VRAM at Q4_K_M — 5.2 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	5.2 GB	6.7 GB	good
Q8_0	8.8 GB	10.3 GB	high
FP16	16 GB	17.5 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	CPU offload
RTX 4060 Laptop	8 GB	16 GB	Tight
RTX 3060 (12GB)	12 GB	32 GB	Comfortable
RTX 4060 Ti (16GB)	16 GB	32 GB	Comfortable
RTX 3090	24 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.

Check Ministral 8B against your exact hardware How these verdicts are calculated

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VRAM fit by quantization level

Enter your GPU VRAM below to see which quantization of Ministral 8B fits. This catalog has no verified local Ollama tag for this checkpoint, so fit grades do not include a local run command.

Sources to verify

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Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Mistral family hub AI tools directory Model source notes Models directory Guides library

Hardware~4.9 GB at Q4_K_M (8B parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext32,768 tokensLast updated2026

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