Mixtral 8x7B Instruct v0.1 — Hardware Requirements & GPU Compatibility
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Mixtral 8x7B Instruct v0.1 is Mistral AI's flagship Mixture-of-Experts model, combining eight expert networks of 7 billion parameters each for a 46.7B total weight count while activating only about 12.9 billion parameters per token. This sparse architecture delivers performance that rivals much larger dense models at a fraction of the inference cost, excelling across reasoning, code generation, and multilingual tasks. Because the full weights must still be loaded into memory, you will need around 24–48 GB of VRAM depending on quantization level, making it best suited for multi-GPU desktop setups or high-VRAM workstation cards. If your hardware can accommodate it, Mixtral offers one of the best performance-per-active-parameter ratios available for local deployment.
693.5K downloads
4.6K 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 Instruct 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 |
| Q3_K_L | 4.10 | 24.5 GB | 28.5 GB | 23.94 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 25.7 GB | 29.7 GB | 25.10 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 26.8 GB | 30.9 GB | 26.27 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 28.6 GB | 32.6 GB | 28.02 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 29.8 GB | 33.8 GB | 29.19 GB | 5-bit legacy quantization |
| Q5_K_S | 5.50 | 32.7 GB | 36.7 GB | 32.11 GB | 5-bit small quantization |
| 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 Instruct v0.1?
Q4_K_M · 28.6 GBMixtral 8x7B Instruct 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 Instruct v0.1?
Q4_K_M · 28.6 GB15 devices with unified memory can run Mixtral 8x7B Instruct 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 tightRelated Models
Derivatives (3)
Frequently Asked Questions
- How much VRAM does Mixtral 8x7B Instruct v0.1 need?
Mixtral 8x7B Instruct 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 Instruct 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 Instruct v0.1?
For Mixtral 8x7B Instruct 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 Q2_K at 20.4 GB.
VRAM requirement by quantization
Q2_K20.4 GB~75%Q4_023.9 GB~85%Q4_K_S26.8 GB~88%Q4_K_M ★28.6 GB~89%Q5_K_S32.7 GB~92%Q8_047.3 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Mixtral 8x7B Instruct v0.1 on a Mac?
Mixtral 8x7B Instruct v0.1 requires at least 20.4 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 8x7B Instruct v0.1 locally?
Yes — Mixtral 8x7B Instruct 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 Instruct v0.1?
At Q4_K_M, Mixtral 8x7B Instruct 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)
AMD Instinct MI300X~102 tok/sNVIDIA H100 SXM~76 tok/sAMD Instinct MI250X~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 Instruct 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 (Q2_K) is 19.85 GB.