Run Mixtral 8x7B Instruct v0.1 on Mac
Mixtral 8x7B Instruct v0.1 runs 100% private on Mac inside Private LLM — no internet connection required, no data sent to any server.
Download the quantized weights
These are the exact OmniQuant weights Private LLM runs for Mixtral 8x7B Instruct v0.1, published on our Hugging Face org. They're standard weights you can load in any app that supports the format, not just Private LLM.
Specifications
- Parameters
- 46.7B
- Context window
- 33K tokens
- License
- Apache 2.0
- Quantization
- OmniQuant 4-bit
- Family
- Mixtral 8x7B
What Mixtral 8x7B Instruct v0.1 is good at
Mixtral 8x7B is a large language model that uses a sparse mixture of experts design. It is good at generating text and following instructions in a chat format. The model outperforms Llama 2 70B on many benchmarks.
Which of your devices can run it
Mac
How to run Mixtral 8x7B Instruct v0.1 in Private LLM
- Download Private LLM from the App Store.
- Open the in-app model library and choose Mixtral 8x7B Instruct v0.1.
- Download the model once, then chat fully offline.
Variants & related models
Frequently asked questions
Mixtral 8x7B Instruct v0.1 is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline.
Yes. Mixtral 8x7B Instruct v0.1 runs on Macs with enough unified memory, such as Mac (Apple Silicon, 192GB), MacBook Pro (M4 Max, 128GB), Mac Studio / Pro (Apple Silicon, 96GB), MacBook Pro (M4 Max, 64GB), MacBook Pro (M4 Max, 48GB), MacBook Pro (M4 Max, 36GB), Mac (Apple Silicon, 32GB), fully on-device in Private LLM.
Yes. Once downloaded in Private LLM, Mixtral 8x7B Instruct v0.1 runs 100% on-device — no internet connection, and nothing is sent to any server.
Private LLM is a one-time purchase with no subscription and no per-message cost. The models themselves are open source — once downloaded, they run offline with nothing to pay per use.
Why run Mixtral 8x7B Instruct v0.1 in Private LLM
Private LLM has run local AI on iPhone, iPad, and Mac since 2023, before Apple Intelligence existed. Inference happens on your device, so your Mixtral 8x7B Instruct v0.1 conversations never reach a server. The part most apps gloss over is quantization, and that is exactly where on-device quality is won or lost. Most llama.cpp and MLX wrappers ship the same off-the-shelf 4-bit RTN weights. Private LLM ships GPTQ and OmniQuant quantization, tuned per model, and our 3-bit OmniQuant models match or beat those 4-bit RTN builds on the same Apple Silicon. Run the same model both ways and you feel it in the first reply. See how our quantization works.
Specifications and summary come from Mixtral 8x7B Instruct v0.1's Hugging Face model card, released under the Apache 2.0 license. Private LLM ships its own quantized models, built with OmniQuant quantization tuned per model, and isn't affiliated with the model's authors.