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How to run Mixtral 8x7B Instruct locally

Mixtral 8x7B Instruct (Mistral AI, 46.7B) has a calculated baseline of about 34 GB of VRAM at Q4_K_M. Actual fit depends on the exact build, runner, context, and overhead. If it fits, marginal compute can be $0 on hardware you already own; electricity and allocated hardware cost are separate.

Calculated VRAM
~34 GB
Download
~26 GB
Quant
Q4_K_M
Context
32.8K

Three ways to run Mixtral 8x7B Instruct locally

1. Ollama — the one-liner

Auto-downloads a 4-bit quant and starts a chat — the simplest option.

ollama run mixtral:8x7b

First run downloads ~26 GB, then it can run offline. Electricity and hardware costs still apply. Get Ollama at ollama.com.

2. LM Studio — point-and-click

Open LM Studio, search “Mixtral 8x7B Instruct”, and choose a quant within your VRAM budget. The ≈34 GB figure at Q4_K_M is a calculated baseline, so leave headroom for context and runner overhead. Load it and chat offline. It also serves a local OpenAI-compatible API you can point Spanvero at.

3. llama.cpp — maximum control

Grab a community GGUF build of Mixtral 8x7B Instruct from Hugging Face (search “Mixtral 8x7B Instruct GGUF” — bartowski and unsloth publish reliable ones), then run:

./llama-cli -m <Q4_K_M-file>.gguf -p "Hello" -ngl 99

Or serve it with ./llama-server -m <file>.gguf for an OpenAI-compatible API on :8080.

Cost recap — full breakdown on the Mixtral 8x7B Instruct cost page

Rent a GPU on your own account: RunPod · Vast — you pay their normal price; disclosed referral links. How we stay honest.

License: commercial use OK.

Browse: Mixtral 8x7B Instruct cost · models for your GPU · all models

Open the free LLM cost calculator → to compare editable local, rented-GPU, and API assumptions.

Prices as of 2026-08-24. $0 markup, your own accounts, we never resell compute. © 2026 Cynosure LLC.

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