[Models](/models) / [Mixtral 8x7B](/models?family=Mixtral%208x7B#models-gallery) / Mixtral 8x7B Instruct v0.1 ![Mistral logo](/model-logos/mistral.svg) # 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. 46.7BMac ## 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. - [Mac — 4-bit OmniQuant →](https://huggingface.co/numen-tech/Mixtral-8x7B-Instruct-v0.1-w4a16g128asym_1) ## 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 general 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 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) Browse every model that fits [iPhone](/models/best-for-iphone) or [Mac](/models/best-for-mac). ## How to run Mixtral 8x7B Instruct v0.1 in Private LLM 1. Download Private LLM from the App Store. 2. Open the in-app model library and choose Mixtral 8x7B Instruct v0.1. 3. Download the model once, then chat fully offline. [![Download Private LLM on the App Store](/app-store/download-badge/en/download.svg)![Download Private LLM on the App Store](/app-store/download-badge/en/download-dark.svg)](/download)[Join our Discord](/discord) ## Variants & related models ![Mistral logo](/model-logos/mistral.svg) ### Dolphin 2.6 Mixtral 8x7B 56B uncensored 33K contextMac [View details →](/models/dolphin-2.6-mixtral-8x7b) ![Mistral logo](/model-logos/mistral.svg) ### Nous Hermes 2 Mixtral 8x7B DPO 46.7B general 33K contextMac [View details →](/models/nous-hermes-2-mixtral-8x7b-dpo) ![Meta logo](/model-logos/meta.svg) ### Airoboros l2 7b 3.0 6.7B general 4K contextiPhone & iPad · Mac [View details →](/models/airoboros-l2-7b-3.0) ![Mistral logo](/model-logos/mistral.svg) ### Airoboros M 7B 7.2B general 33K contextMac [View details →](/models/airoboros-m-7b-3.1.2) ![Meta logo](/model-logos/meta.svg) ### Cat Llama 3 70B Instruct 70.6B general 8K contextMac [View details →](/models/cat-llama-3-70b-instruct) ![Google Gemma logo](/model-logos/gemma.svg) ### FuseChat Gemma 2 9B Instruct 9.2B general 8K contextiPhone & iPad · Mac [View details →](/models/fusechat-gemma-2-9b-instruct) ## Frequently asked questions - Can I run Mixtral 8x7B Instruct v0.1 on iPhone? Mixtral 8x7B Instruct v0.1 is too large for current iPhones. It runs on Mac with enough unified memory inside Private LLM, fully offline. - Can I run Mixtral 8x7B Instruct v0.1 on Mac? 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. - Does Mixtral 8x7B Instruct v0.1 work offline? 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. - Is Mixtral 8x7B Instruct v0.1 free to use? 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](/en/faq#Why-can-t-Private-LLM-load-models-directly-from-Hugging-Face). ### More from Numen [![Slop or Not app icon](/app-icons/slop-or-not.png) #### Slop or Not Catch AI-written text on iPhone and Mac. The check runs on-device, so nothing you paste gets uploaded. ](https://slopornot.ai)[![Clean Links app icon](/app-icons/clean-links.png) #### Clean Links Strip trackers and clutter from any link before you share it. The cleanup happens on your device. ](https://cleanlinks.app) Specifications and summary come from Mixtral 8x7B Instruct v0.1's [Hugging Face model card](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), 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.