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, plus a fixed overhead for KV-cache, activations, and fragmentation. 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 | RTX 5060 Ti | 7 | $0.770/hr |
| FP8 (quantized) | 43.5 GB | 52.2 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 4 | $0.440/hr | ||
| INT4 (quantized) | 21.7 GB | 26.1 GB | RTX 4080 Super | 1 | $0.338/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: 7× RTX 5060 Ti, at $0.110/hr per GPU ($0.770/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: common questions
Can Mixtral-8x7B-Instruct-v0.1 run on a single GPU?
No. At BF16 it needs 104.4 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 16.0 GB RTX 5060 Ti, and it takes 7 of them.
How many GPUs do I need to run Mixtral-8x7B-Instruct-v0.1?
7 at BF16. It needs 104.4 GB of VRAM and the cheapest capable live offer is a 16.0 GB RTX 5060 Ti, so 7 of them come to $0.770/hr in total.
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 7 RTX 5060 Ti cards at $0.770/hr. At INT4 (quantized) it drops to one RTX 4080 Super at $0.338/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 7Run 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 (7× RTX 5060 Ti 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.
More Mixtral models
- Mixtral-8x7B-Instruct-v0.1 (46.7B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
