LocalClaw/LLM/Mixtral (8x7B)
Open-weight local LLM

Mixtral (8x7B)

Mistral's MoE pioneer. 46.7B total, fast inference via sparse activation. Multilingual. 1.4M downloads.

32 GB power user 32 GB RAM Q4_K_M General chat
Run with LocalClaw Compare all models
Parameters
8x7B (46.7B)
Minimum RAM
32 GB
Model size
26 GB
Quantization
Q4_K_M

Can Mixtral (8x7B) run locally?

Mixtral (8x7B) belongs on 32 GB machines when you want stronger quality without jumping to server hardware.

Search for mixtral-8x7b-instruct in LM Studio or another GGUF-compatible runtime.

TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF
chatgeneralpowerquality

Install path

01
Check RAM fitMinimum 32 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch mixtral-8x7b-instruct in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • MoE pioneer
  • Fast despite 46.7B total params
  • Apache 2.0
  • 1.4M downloads
  • Multilingual

Limitations

  • Needs 32GB RAM
  • 32K context limit
  • Superseded by newer MoE models

Best use cases

  • General chat
  • Multilingual tasks
  • Enterprise
  • RAG applications

Capability profile

speed
4
quality
8
coding
8
reasoning
8

Technical notes

Developer
Mistral AI
License
Apache 2.0
Context window
32,768 tokens
Architecture
Sparse Mixture of Experts — 8 experts, 2 active per token

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Comfortable headroomMac mini M4 Pro 48GBMobile workstationMacBook Pro M4 Max 36GBPower-user picks32GB RAM guide

Similar models to compare

Mistral Small (24B) 24BDolphin Mixtral (8x7B) 8x7B (46.7B)Command R (35B) 35B

Where to go next