What GPU do I need to run mistralai/Mixtral-8x7B-Instruct-v0.1?
A 46.7B (MoE) language model for chat and instruction-following. 46.7B parameters, published in BF16. View on Hugging Face
Mixtral-8x7B-Instruct-v0.1 is published by mistralai on Hugging Face, with 213,678 downloads and 4,756 likes to date. It's a MixtralForCausalLM model built for text-generation, published natively in BF16.
What Mixtral-8x7B-Instruct-v0.1 is
Mixtral-8x7B-Instruct-v0.1 is a 46.7B-parameter mixture-of-experts language model published by Mistral AI on Hugging Face. It is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from Mixtral-8x7B-Instruct-v0.1's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 87.0 GB | 104.4 GB | AMD MI300X | 1 | $2.63/hr |
| cheaper alt. | RTX 5060 Ti | 7 | $0.770/hr | ||
| FP8 (quantized) | 43.5 GB | 52.2 GB | RTX PRO 6000 | 1 | $1.51/hr |
| cheaper alt. | RTX 5060 Ti | 4 | $0.440/hr | ||
| INT4 (quantized) | 21.7 GB | 26.1 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Mixtral-8x7B-Instruct-v0.1 at its published (BF16) precision: 1× AMD MI300X, at $2.63/hr per GPU ($2.63/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Mixtral-8x7B-Instruct-v0.1: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 131,072 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.50 GB |
| 32K | 4.0 GB |
Computed from the layer, head and window counts in the model's own configuration (mistralai/Mixtral-8x7B-Instruct-v0.1), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Mixtral-8x7B-Instruct-v0.1: common questions
Can Mixtral-8x7B-Instruct-v0.1 run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 104.4 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 192.0 GB AMD MI300X at $2.63/hr.
What is the least VRAM Mixtral-8x7B-Instruct-v0.1 can run in?
26.1 GB, at INT4 (quantized), which fits a 32 GB card, against 104.4 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Mixtral-8x7B-Instruct-v0.1 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one AMD MI300X at $2.63/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
How to run Mixtral-8x7B-Instruct-v0.1
Run Mixtral-8x7B-Instruct-v0.1 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve mistralai/Mixtral-8x7B-Instruct-v0.1 --tensor-parallel-size 1Run Mixtral-8x7B-Instruct-v0.1 with GGUF quantizations
Prebuilt GGUF weights published at TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUFSource: https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF
Deploy Mixtral-8x7B-Instruct-v0.1 on Aquanode
Aquanode has no one-click deploy template for Mixtral-8x7B-Instruct-v0.1; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× AMD MI300X or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Lineage
Also published as: RedHatAI/Mixtral-8x7B-Instruct-v0.1. Identical copies of this model under another account.
More on Mixtral-8x7B-Instruct-v0.1
Fits on a 141 GB GPU at BF16: every model that fits in 141 GB.
Fits on an 80 GB GPU at FP8: every model that fits in 80 GB.
Fits on a 32 GB GPU at INT4: every model that fits in 32 GB.
Related reading: AMD MI300X pricing and specs, How much VRAM you need for LLMs, and What AI inference is.
