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.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 85 GB | 88 GB | 96 GB | 85% |
Context window & KV cache
Adds 3.00 GB to VRAMLong 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
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 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.
Self-host serving plan
Want to host Mixtral 8x22B Instructfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
91.5 GB
88.0 GB weights + 3.0 GB KV
Aggregate tok/s
2
across 1 user
Per-user tok/s
2
MoE active params
⚠ Will spill 67.5 GB of weights to system RAM (~5× slower per offloaded layer). Use llama.cpp’s --cpu-offload-gb or vLLM’s --swap-space.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Mixtral 8x22B Instruct?
Mixtral 8x22B Instruct requires 88 GB VRAM minimum with Q4_K_M quantization. For full precision you need 88 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Mixtral 8x22B Instruct?
To run Mixtral 8x22B Instruct, you need a GPU with at least 88 GB of VRAM, such as the NVIDIA A100 or H100.
Is Mixtral 8x22B Instruct good for coding?
Yes, Mixtral 8x22B Instruct is well-suited for coding tasks due to its large context length of 65,536 tokens and strong language understanding capabilities.
Mixtral 8x22B Instruct vs Llama 3.1 8B?
Mixtral 8x22B Instruct has significantly more parameters (141B vs 8B) and a longer context length (65,536 vs 2,048 tokens), making it more powerful but requiring more VRAM.
Can I run Mixtral 8x22B Instruct on a Mac?
Running Mixtral 8x22B Instruct on a Mac is possible if your Mac has a compatible GPU with at least 88 GB of VRAM, which is rare. Most Macs will struggle with this requirement.
How much VRAM does Mixtral 8x22B Instruct need?
Mixtral 8x22B Instruct requires 88 GB of VRAM, regardless of quantization, to run efficiently.
Is Mixtral 8x22B Instruct censored?
No, Mixtral 8x22B Instruct is not censored. It is designed to provide open and unrestricted responses, but it may still have content filters in place to prevent harmful outputs.
Is Mixtral 8x22B Instruct commercial-use allowed?
Yes, Mixtral 8x22B Instruct is licensed under the Apache-2.0 license, which allows for commercial use without additional fees.
Mixtral 8x22B Instruct context length?
The context length for Mixtral 8x22B Instruct is 65,536 tokens, allowing it to process very long sequences of text.
Does Mixtral 8x22B Instruct support function calling?
Yes, Mixtral 8x22B Instruct supports function calling, enabling it to interact with external systems and perform complex tasks.
Mixtral 8x22B Instruct quantization options?
Mixtral 8x22B Instruct can be quantized to 8-bit or 4-bit precision to reduce VRAM usage, but it still requires 88 GB of VRAM even after quantization.
Can Mixtral 8x22B Instruct run on CPU?
While theoretically possible, running Mixtral 8x22B Instruct on a CPU is highly impractical due to its massive size and computational requirements.
Mixtral 8x22B Instruct fine-tuning?
Fine-tuning Mixtral 8x22B Instruct is possible but requires significant computational resources and expertise. It is recommended for advanced users with access to powerful hardware.
Mixtral 8x22B Instruct system requirements?
To run Mixtral 8x22B Instruct, you need a system with at least 88 GB of VRAM, 512 GB of RAM, and a multi-core CPU. SSD storage is also recommended for faster loading times.
Mixtral 8x22B Instruct performance benchmark?
Performance benchmarks for Mixtral 8x22B Instruct show it can process around 50-70 tokens per second on an NVIDIA A100 GPU, depending on the task complexity and quantization level.
Mixtral 8x22B Instruct for RAG?
Yes, Mixtral 8x22B Instruct is suitable for Retrieval-Augmented Generation (RAG) tasks due to its large context length and ability to handle complex queries.
Mixtral 8x22B Instruct for agents?
Mixtral 8x22B Instruct is well-suited for creating intelligent agents due to its advanced language capabilities and support for function calling, enabling it to perform a wide range of tasks.
Mixtral 8x22B Instruct for coding vs general?
Mixtral 8x22B Instruct performs well in both coding and general tasks, but its large context length and specialized training make it particularly strong for coding applications.
Mixtral 8x22B Instruct vs ChatGPT?
Mixtral 8x22B Instruct has more parameters (141B vs 175B for the largest ChatGPT model) and a longer context length (65,536 vs 4,096 tokens), making it more powerful for certain tasks but requiring more VRAM.
Mixtral 8x22B Instruct download size?
The download size for Mixtral 8x22B Instruct is approximately 282 GB for the full model, which can be reduced with quantization.
Best quant for Mixtral 8x22B Instruct?
The best quantization for Mixtral 8x22B Instruct depends on your use case. 8-bit quantization is a good balance between performance and VRAM usage, while 4-bit quantization can further reduce VRAM requirements.