Mixtral 8x22B v0.1 — Hardware Requirements & GPU Compatibility
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Mixtral 8x22B v0.1 is a 140.6B-parameter open language model from Mistral AI in the Mixtral family. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 85.14 GB of VRAM — see which GPUs and Macs can run it below.
16.3K downloads
239 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 ~52 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 = ~61 tok/s
Estimated speed at Q4_K_M (85.1 GB)
~61 tok/s~61 tok/s~52 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.