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Apache 2.0Open weights where releasedUpdated August 2026

Mixtral 8x7B Instruct

Mixtral 8x7B Instruct is a Mistral-family model worth evaluating for moe local and hosted baseline 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

MoE local and hosted baseline workflows

Who should use it

  • MoE local and hosted baseline workflows
  • Builders who want local or self-hosted testing options.

Common workflows

  • MoE local and hosted baseline workflows
  • chat 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.
  • MoE local and hosted baseline 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 Mixtral 8x7B Instruct run on your machine?

Mixtral 8x7B Instruct is 46.7B parameters and needs 30 GB of VRAM at Q4_K_M — 28.5 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_M28.5 GB30 GBgood
Q8_050 GB51.5 GBhigh
FP1691 GB92.5 GBreference

Fit on common hardware at Q4_K_M

HardwareMemory the model can useSystem RAMVerdict
CPU OnlyNone (CPU only)16 GBToo large
RTX 4060 Laptop8 GB16 GBToo large
RTX 3060 (12GB)12 GB32 GBToo large
RTX 4060 Ti (16GB)16 GB32 GBToo large
RTX 309024 GB64 GBCPU offload
Apple Silicon (Unified Memory) 36 GB27 GB of 36 GB36 GBToo large
RTX 509032 GB64 GBComfortable
Apple Silicon (Unified Memory) 48 GB36 GB of 48 GB48 GBComfortable
Apple Silicon (Unified Memory) 64 GB48 GB of 64 GB64 GBComfortable

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.

Check Mixtral 8x7B Instruct against your exact hardwareHow these verdicts are calculated

Need more hardware for Mixtral 8x7B Instruct? Open the PC Builder for the MoE / frontier-style experiment tier →

VRAM fit by quantization level

Enter your GPU VRAM below to see which quantization of Mixtral 8x7B Instruct fits and get the Ollama run command.

Sources to verify

Exact model card

Related resources

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

Mistral family hubBest local LLM toolsPrivate local chatbot stackOllama + Open WebUI starter stackAI tools directory
Hardware~28.3 GB at Q4_K_M (46.7B total parameters, MoE)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext32,768 tokensLast updated2026
Exact model card →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

Check if this model fits your GPU →What can 32GB VRAM run? →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

Mistral family hub →Pixtral 12B →Pixtral Large →Codestral 22B →Phi-3 Medium →Qwen2.5 14B →DeepSeek V3 →

Guides, stacks, and comparisons

Best local LLM tools →Private local chatbot stack →Ollama + Open WebUI starter stack →All model comparisons →

Related models

Pixtral 12BVision-language local and hosted experimentsPixtral LargeVision-language assistant workflowsCodestral 22BCoding assistant workflowsPhi-3 MediumLocal assistant, reasoning, and long-context RAG workflows on 12–16 GB VRAM GPUs (RTX 3080/4070/4080). Good step up from 7B without requiring multi-GPU.Qwen2.5 14BBuilders hosting local AI on a single RTX 4080 (16 GB) or RTX 4090 (24 GB) who want near-frontier multilingual reasoning, mathematical accuracy, and agentic coding performance without multi-GPU infrastructure. The sweet spot between consumer accessibility and frontier-class open-weight capability for 2026 local deployment.DeepSeek V3General DeepSeek-family comparisons and assistant workflow evaluation.

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