Mixtral 8x22B v0.1 — Hardware Requirements & GPU Compatibility
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Mixtral-8x22B-v0.1 is Mistral AI's large sparse mixture-of-experts base model, a pretrained checkpoint with no instruction tuning and no built-in moderation, intended as the foundation for fine-tuned or instruct derivatives rather than direct chat use. It routes each token through 2 of 8 experts, giving roughly 39 billion active parameters out of about 141 billion total, so its per-token compute is far lighter than its total size at the cost of having to hold every expert in memory. Its pretraining data covers English, French, German, Spanish, and Italian. Because all experts must be resident even though only a fraction activate per token, it still needs a multi-GPU workstation even when quantized. Context length is 65,536 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in April 2024, ahead of an instruction-tuned Mixtral-8x22B-Instruct release.
13.9K downloads
241 likesSpecifications
- Publisher
- Mistral AI
- Family
- Mixtral
- Parameters
- 140.6B
- Architecture
- MixtralForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-04-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mixtral 8x22B v0.1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 60.5 GB | 75.1 GB | 59.76 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 69.3 GB | 83.9 GB | 68.55 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 85.1 GB | 99.7 GB | 84.37 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 101.0 GB | 115.5 GB | 100.19 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 116.8 GB | 131.3 GB | 116.01 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 141.4 GB | 155.9 GB | 140.62 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 282.0 GB | 296.6 GB | 281.24 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Mixtral 8x22B v0.1?
Q4_K_M · 85.1 GBMixtral 8x22B v0.1 (Q4_K_M) requires 85.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 111+ GB is recommended. Using the full 66K context window can add up to 14.6 GB, bringing total usage to 99.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Mixtral 8x22B v0.1?
Q4_K_M · 85.1 GB18 devices with unified memory can run Mixtral 8x22B v0.1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M5 Max (128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Mixtral 8x22B v0.1
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Mixtral 8x22B v0.1 need?
Mixtral 8x22B v0.1 requires 85.1 GB of VRAM at Q4_K_M, or 282.0 GB at BF16. Full 66K context adds up to 14.6 GB (99.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 140.6B × 4.8 bits ÷ 8 = 84.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 15.3 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M85.1 GBQ4_K_M + full context99.7 GB- Can NVIDIA GeForce RTX 5090 run Mixtral 8x22B v0.1?
No — Mixtral 8x22B v0.1 requires at least 60.5 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Mixtral 8x22B v0.1?
For Mixtral 8x22B v0.1, Q4_K_M (85.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (101.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 60.5 GB.
VRAM requirement by quantization
Q2_K60.5 GBQ4_K_M ★85.1 GBQ5_K_M101.0 GBQ6_K116.8 GBQ8_0141.4 GBBF16282.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mixtral 8x22B v0.1 on a Mac?
Mixtral 8x22B v0.1 requires at least 60.5 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Mixtral 8x22B v0.1 locally?
Yes — Mixtral 8x22B v0.1 can run locally on consumer hardware. At Q4_K_M quantization it needs 85.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mixtral 8x22B v0.1?
At Q4_K_M, Mixtral 8x22B v0.1 can reach ~62 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 85.1 × 0.65 = ~131 tok/s
Estimated speed at Q4_K_M (85.1 GB)
~131 tok/s~131 tok/s~95 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mixtral 8x22B v0.1?
At Q4_K_M, the download is about 84.37 GB. The full-precision BF16 version is 281.24 GB. The smallest option (Q2_K) is 59.76 GB.
- Which GPUs can run Mixtral 8x22B v0.1?
No single consumer GPU has enough VRAM to run Mixtral 8x22B v0.1 at Q4_K_M (85.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run Mixtral 8x22B v0.1?
19 devices with unified memory can run Mixtral 8x22B v0.1 at Q4_K_M (85.1 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.