Best GPU for Mixtral Hosting
Mixtral is a Mixture-of-Experts model — 47B total parameters, 12.9B active per token. Quality matches a 70B dense model, but the full 94 GB FP16 footprint demands a real GPU.
The short answer: the RTX 6000 Pro 96 GB is the best GPU for self-hosting Mixtral 8x7B on a dedicated server. It has the right VRAM (96 GB) for the model, modern tensor cores, and the best cost-per-token in our catalogue for this workload.
Ranking — Best to Worst for This Workload
From best to worst for this specific workload, with the reason in plain English.
RTX 6000 Pro 96 GB Top Pick
96 GB fits Mixtral 8x7B FP16 single-card. The cleanest deployment.
96 GB · Blackwell · from £1099/mo
RTX 5090 INT4 Pick
32 GB fits Mixtral 8x7B INT4 (AWQ). Some quality drop but workable.
32 GB · Blackwell · from £359/mo
A100 80 GB Datacenter
80 GB FP16 with KV cache headroom — production reference.
80 GB · Ampere · POA
RTX 3090 Multi-GPU
2× RTX 3090 = 48 GB combined, fits Mixtral 8x7B at FP8.
24 GB · Ampere · from £179/mo
RTX 5080 Not Recommended
16 GB too tight even at INT4. Skip.
16 GB · Blackwell · from £189/mo
Background & Sizing
Mixtral 8x7B is Mistral AI’s flagship Mixture-of-Experts model. The architecture loads 8 separate 7B "experts" and routes each token to two of them. The result: throughput similar to a 13B model with quality similar to a 70B model, at the cost of 94 GB of VRAM at FP16.
For self-hosting, the practical choices are: a single RTX 6000 Pro 96 GB for FP16, a single RTX 5090 for INT4, or a multi-GPU cluster (2× RTX 5090, 4× RTX 3090, 2× A100) for distributed serving.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Mixtral 8x7B vs Llama 3 70B — which to host?
Mixtral is faster (12.9B active vs 70B dense) and similar quality. Llama 3 70B is broadly stronger on long-context. Mixtral is cheaper to host.
Mixtral 8x22B?
141B total, 39B active. Needs 282 GB FP16 — multi-GPU 6000 Pro or A100 cluster only.
Why is Mixtral so VRAM-hungry?
All 8 experts have to be loaded into VRAM, even though only 2 are active per token. You pay for all the parameters at memory time.
Can MoE quantisation help?
Yes. AWQ-INT4 brings Mixtral 8x7B down to ~26 GB — fits comfortably on a 5090.
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