Mixtral 8x7B Instruct
Mixtral 8x7B Instruct loads in 30 GB at Q4_K_M and decodes at the speed of its 13B active path, comfortably from 64 GB of memory, Macs included, starting at the MacBook Pro M5 Pro 48GB. Below is the memory math we ran for it, the smallest card that fits, and the fastest machine we track.
How much memory does Mixtral 8x7B Instruct need?
Mixtral 8x7B Instruct is a 46.7B mixture-of-experts model (13B active per token) built for coding, quality. The weights alone take 30 GB at Q4_K_M; add context and the OS, and it belongs in the 64 GB memory class. Every number below comes from the ModelFit engine: speeds are estimates, labeled est., because we do not benchmark hardware ourselves.
KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).
Which machines run Mixtral 8x7B Instruct?
Verdicts come from the same engine that powers our fit checker: memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card
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Frequently asked questions
How much memory does Mixtral 8x7B Instruct need?
30 GB for the Q4_K_M weights, plus ~5.0 GB of KV-cache at 16k context, about 35.0 GB total. Comfortable from 64 GB of VRAM or unified memory.
Does Mixtral 8x7B Instruct run on a Mac?
Yes, from the MacBook Pro M5 Pro 48GB (~16 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the smallest GPU for Mixtral 8x7B Instruct?
The NVIDIA RTX PRO 6000 Blackwell (96 GB VRAM) is the smallest tracked card that runs Mixtral 8x7B Instruct comfortably, ~56 tok/s est.
Cite this page
ModelFit: Mixtral 8x7B Instruct, specs, memory math and hardware verdicts. https://modelfit.io/models/mixtral-8x7b/ (dataset updated 2026-10-13, CC BY 4.0).