Mixtral 8x7B v0.1 — Hardware Requirements & GPU Compatibility
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Mixtral 8x7B v0.1 is a 46.7B-parameter open language model from Mistral AI in the Mixtral family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 28.59 GB of VRAM — see which GPUs and Macs can run it below.
76.9K downloads
1.8K likesSpecifications
- Publisher
- Mistral AI
- Family
- Mixtral
- Parameters
- 46.7B
- Architecture
- MixtralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,000
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Mixtral 8x7B v0.1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 20.4 GB | 24.4 GB | 19.85 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 21 GB | 25.0 GB | 20.43 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 23.3 GB | 27.4 GB | 22.77 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 23.9 GB | 27.9 GB | 23.35 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 28.6 GB | 32.6 GB | 28.02 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 33.8 GB | 37.9 GB | 33.28 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 39.1 GB | 43.1 GB | 38.53 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 47.3 GB | 51.3 GB | 46.70 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mixtral 8x7B v0.1?
Q4_K_M · 28.6 GBMixtral 8x7B v0.1 (Q4_K_M) requires 28.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 38+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 32.6 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mixtral 8x7B v0.1?
Q4_K_M · 28.6 GB15 devices with unified memory can run Mixtral 8x7B v0.1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
View all 3 →Related Models
Derivatives (5)
Frequently Asked Questions
- How much VRAM does Mixtral 8x7B v0.1 need?
Mixtral 8x7B v0.1 requires 28.6 GB of VRAM at Q4_K_M, or 47.3 GB at Q8_0. Full 33K context adds up to 4.0 GB (32.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 46.7B × 4.8 bits ÷ 8 = 28 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M28.6 GBQ4_K_M + full context32.6 GB- Can NVIDIA GeForce RTX 4090 run Mixtral 8x7B v0.1?
Yes, at Q4_0 (23.9 GB) or lower. Higher quantizations like Q3_K_L (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Mixtral 8x7B v0.1?
For Mixtral 8x7B v0.1, Q4_K_M (28.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (29.8 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 19.8 GB.
VRAM requirement by quantization
IQ3_XS19.8 GBIQ3_M21.6 GBIQ4_XS25.7 GBQ4_K_M ★28.6 GBQ5_029.8 GBQ8_047.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mixtral 8x7B v0.1 on a Mac?
Mixtral 8x7B v0.1 requires at least 19.8 GB at IQ3_XS, 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 8x7B v0.1 locally?
Yes — Mixtral 8x7B v0.1 can run locally on consumer hardware. At Q4_K_M quantization it needs 28.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mixtral 8x7B v0.1?
At Q4_K_M, Mixtral 8x7B v0.1 can reach ~102 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 28.6 × 0.55 = ~102 tok/s
Estimated speed at Q4_K_M (28.6 GB)
~102 tok/s~76 tok/s~63 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mixtral 8x7B v0.1?
At Q4_K_M, the download is about 28.02 GB. The full-precision Q8_0 version is 46.70 GB. The smallest option (IQ3_XS) is 19.26 GB.
- Which GPUs can run Mixtral 8x7B v0.1?
1 consumer GPU can run Mixtral 8x7B v0.1 at Q4_K_M (28.6 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Mixtral 8x7B v0.1?
15 devices with unified memory can run Mixtral 8x7B v0.1 at Q4_K_M (28.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.