Qwen3.8-Flash-Next
Qwen3.8-Flash-Next loads in 123 GB at IQ1_S and decodes at the speed of its 6B active path, comfortably from 192 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 Qwen3.8-Flash-Next need?
Qwen3.8-Flash-Next is a 125B mixture-of-experts model (6B active per token) built for agentic coding, reasoning, multimodal. The weights alone take 123 GB at IQ1_S; add context and the OS, and it belongs in the 192 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 Qwen3.8-Flash-Next?
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
More Qwen models
Frequently asked questions
How much memory does Qwen3.8-Flash-Next need?
123 GB for the IQ1_S weights, plus ~6.0 GB of KV-cache at 16k context — about 129.0 GB total. Comfortable from 192 GB of VRAM or unified memory.
Does Qwen3.8-Flash-Next run on a Mac?
Yes — from the Mac Studio M5 Ultra 256GB (~32 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the cheapest GPU for Qwen3.8-Flash-Next?
No tracked consumer GPU runs Qwen3.8-Flash-Next comfortably.
Cite this page
ModelFit: Qwen3.8-Flash-Next — specs, memory math and hardware verdicts. https://modelfit.io/models/qwen3.8-flash-next/ (dataset updated 2026-09-03, CC BY 4.0).