Run Mixtral on Your Machine

Run Your Own AI (Mixtral) on Your Machine

Discover how hosting Mixtral, an open-source large language model, on a cloud instance can help you own your AI.

Have you ever wondered how to maximize your control over an AI system while ensuring scalability and customization? Running Mixtral LLM on your own cloud instance not only gives you unparalleled flexibility but also enhances your data security. This post explores how to deploy Mixtral Instruct using llama.cpp on a cloud instance, enabling you to own your AI.

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Mixtral Instruct v0.1

Mixtral1 represents a significant leap in the open-source AI landscape, boasting top-tier performance as a sparse mixture of experts model (MoE) that excels in both speed and accuracy. Licensed under the permissive Apache 2.0, it offers an impressive balance of cost and performance, matching or surpassing benchmarks set by models like GPT-3.5.

  • Handles 32k tokens
  • Multilingual support (English, German, French, Italian, Spanish)
  • Finetuned into a instruction following model
  • About 45B parameters (not 56B as you might expect from the name)

Cloud Instance Setup

To run Mixtral on a cloud instance, you need to set up a virtual machine (VM). In this tutorial, we'll use a Runpod2 instance with a GPU. Of course, any other cloud provider like AWS, GCP, or Azure would work as well.

Here are the steps to set up a cloud instance:

  1. Sign up for a Runpod account
  2. Go to (opens in a new tab) and create a new instance with a GPU (I'll use A6000 with 48GB VRAM)
  3. Select Pytorch 2.2.0 as the Pod template and configure it as follows:

Pod config

Pod config
  1. Enable SSH Terminal Access and Start Jupyter Notebook
  2. Click Deploy and wait for the instance to be ready

Pod running

Pod running

Install llama.cpp

Running Mixtral would require about 90GB of VRAM in half precision (FP16) - 2 bytes for each parameter (45B parameters). Luckily, the llama.cpp library can help you run Mixtral on a GPU with 48GB of VRAM.

Llama.cpp3 is a highly optimized library designed to facilitate large language model (LLM) inference across diverse platforms, that is fast (tokens/second) inference and low resource usage. Most people use it to run LLMs on CPUs, but it also works wonders on GPUs.

Let's start by installing the Python bindings for llama.cpp. You can install llama-cpp-python from PyPI. In the terminal of your cloud instance, run the following commands:

CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python==0.2.61
pip install huggingface_hub==0.22.2

Then connect to the Jupyter Lab from the machine options and create a new notebook.

Load the Model

To run Mixtral with llama.cpp we need a model in GGUF format. You can download the Mixtral Instruct v0.14 model from the Hugging Face Hub. Here's how you can do it:

import os
from huggingface_hub import hf_hub_download
HF_REPO_NAME = "TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
HF_MODEL_NAME = "mixtral-8x7b-instruct-v0.1.Q8_0.gguf"
LOCAL_DIR_NAME = "models"
os.makedirs(LOCAL_DIR_NAME, exist_ok=True)
model_path = hf_hub_download(
    repo_id=HF_REPO_NAME, filename=HF_MODEL_NAME, cache_dir=LOCAL_DIR_NAME

The model is almost 50GB, so it might take a while to download. Once you have the model, you can load it using llama.cpp:

from llama_cpp import Llama
llm = Llama(
  n_ctx=2048, # max tokens -> increase to fit more input/output
  n_threads=16, # number of CPU cores
  n_gpu_layers=35 # offload every layer on the GPU

The loading of the model takes a while but once it's done, you should see this as the final output:

Guessed chat format: mistral-instruct

The library correctly guessed the chat format as mistral-instruct. Now you can prompt the model:

    messages = [
            "role": "user",
            "content": "You are a world-renowned paper salesman. Write a single sentence that sells your paper to an AI entity"

Our high-quality, durable paper is perfect for your data processing needs, offering exceptional performance and longevity, ensuring accurate and reliable results for all your AI applications.

Interesting response! Let's have a look at the full output:

Full output
  "id": "chatcmpl-a4cb3d89-df24-4ab1-8e27-83cab52679ea",
  "object": "chat.completion",
  "created": 1713113933,
  "model": "models/models--TheBloke--Mixtral-8x7B-Instruct-v0.1-GGUF/snapshots/fa1d3835c5d45a3a74c0b68805fcdc133dba2b6a/mixtral-8x7b-instruct-v0.1.Q8_0.gguf",
  "choices": [
      "index": 0,
      "message": {
        "role": "assistant",
        "content": " Our high-quality, durable paper is perfect for your data processing needs, offering exceptional performance and longevity, ensuring accurate and reliable results for all your AI applications."
      "logprobs": "None",
      "finish_reason": "stop"
  "usage": {
    "prompt_tokens": 33,
    "completion_tokens": 35,
    "total_tokens": 68

We get roughtly 37 tokens per second on a A6000 GPU. This is a great result considering the model size and the fact that we are running it on a quite old GPU.


Running Mixtral on a cloud instance is a great way to own your AI. It gives you the flexibility to customize the model and the infrastructure to suit your needs. Now it's your turn to explore the possibilities!

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  1. Mixtral (opens in a new tab)

  2. (opens in a new tab)

  3. llama.cpp (opens in a new tab)

  4. Mixtral Instruct v0.1 by TheBloke (opens in a new tab)