Devstral 2 123B
Devstral 2 123B loads in 74.9 GB at Q4_K_M, comfortably from 128 GB of memory, Macs included, starting at the Mac Studio M5 Ultra 256GB. Below is the memory math we ran for it, the cheapest card that fits, and the fastest machine we track.
How much memory does Devstral 2 123B need?
Devstral 2 123B is a 123B dense model built for agentic coding, software engineering. The weights alone take 74.9 GB at Q4_K_M; add context and the OS, and it belongs in the 128 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 Devstral 2 123B?
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 Devstral 2 123B need?
74.9 GB for the Q4_K_M weights, plus ~6.0 GB of KV-cache at 16k context, about 80.9 GB total. Comfortable from 128 GB of VRAM or unified memory.
Does Devstral 2 123B run on a Mac?
Yes, from the Mac Studio M5 Ultra 256GB (~8 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the cheapest GPU for Devstral 2 123B?
The NVIDIA RTX PRO 6000 Blackwell is the cheapest tracked card that runs Devstral 2 123B comfortably, ~14 tok/s est. at ~$12,912 used (as of 2026-07-31).
Cite this page
ModelFit: Devstral 2 123B, specs, memory math and hardware verdicts. https://modelfit.io/models/devstral-2-123b/ (dataset updated 2026-09-29, CC BY 4.0).