DIY CPU/NVMe/GPU hybrid boosts 460GB DeepSeek inference from 216 to 960 tok/s prefill
HankYeomans · x · 2026-09-25
A developer shares hands-on results running DeepSeek's MXFP4 quantized model (460GB) on a custom CPU/NVMe/GPU hybrid inference setup: prefill jumped from 216 to 960 tok/s and decode from 13 to 42 tok/s. The bottleneck is now the CPU/memory/NVMe side — only one GPU hits 100% utilization while the rest idle at 20-30%. The author argues you learn most by watching your own systems fail, and plans to next tackle end-to-end model training, optimization, and ablation from a base checkpoint.
More from Infra
- Gas-for-AI-datacenters forum co-located with CoRL shows energy demand spillover — carlosdponx · 2026-11-12
- a16z podcast: Mike Volpi on Cisco's 70+ acquisition playbook and the AI buildout — a16z · 2026-11-12
- AMD to pass Intel in server revenue by mid-2027 as share gains 6 points a year — BenBajarin · 2026-11-12
- Study: Reflective Data Center Roofs Actually Cool Daytime Ground by 1.8-2.2°C — SumitGup · 2026-11-12
- Tsinghua's BaGuaLu-2 tames MoE expert-load spikes at runtime, accepted to SC26 — jiqizhixin · 2026-11-12
- antirez backs low-bit DeepSeek 4.1 Flash over Qwen 27B dense on Apple M5 Macs — antirez · 2026-11-12