Mistral AI

Mixtral 8x22B Instruct

141 B total / 39 B active MoE. Larger Mixtral; needs serious hardware.

141B parametersmixtralapache-2.064K context88GB - 88GB VRAM

About This Model

Mixtral 8x22B is the bigger sibling of 8x7B — same architecture, just much bigger experts. 88 GB VRAM at Q4 puts it in single-H100 / multi-A100 territory. Active parameter count at 39 B means it still runs faster than a dense 70 B.

Check Your Hardware

See which quantizations of Mixtral 8x22B Instruct your hardware can run.

Quantization Options

QuantizationBitsFile SizeVRAM NeededRAM NeededQuality
Q4_K_M4.585 GB88 GB96 GB
85%

Context window & KV cache

Adds 3.00 GB to VRAM

Long chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.

Model native max: 64K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.

How to run Mixtral 8x22B Instruct

Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.

GUI. Browse → download → chat. MLX on Apple Silicon.

LM Studio home →
  1. 1

    Open LM Studio

    Go to the 🔍 Search tab.

  2. 2

    Search for

    MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUF
  3. 3

    Download

    Pick the Q4_K_M quant — best balance of size vs. quality.

  4. 4

    Chat

    Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.

Community benchmarks

Real tokens/sec reports from people running Mixtral 8x22B Instruct on actual hardware.

No community runs yet for this model. Be the first to submit your numbers.

Download & Run

HF

HuggingFace

View model & download weights

O

Ollama

One-command install & run

See It In Action

Real model outputs generated via RunThisModel.com — watch responses stream in real time.

Llama 3.3 70B responding...
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
Frequently Asked Questions
How much VRAM do I need to run Mixtral 8x22B Instruct?
Mixtral 8x22B Instruct requires 88GB VRAM minimum with Q4_K_M quantization. For full precision, you need 88GB VRAM.
What is the best quantization for Mixtral 8x22B Instruct?
Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.