Analysis of spilling MoE weights onto SSD: GLM-5 is surprisingly usable even with over 1/3rd of weights left on SSD, due to caching dynamics

Reddit r/LocalLLaMA / 4/12/2026

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Key Points

The article analyzes the feasibility of “spilling” Mixture-of-Experts (MoE) model weights from GPU/CPU memory onto SSD storage and finds GLM-5 can still function acceptably even when more than one-third of the weights reside on SSD.
It attributes the surprising usability primarily to caching dynamics, implying that repeated access patterns can mask much of the latency cost of SSD reads.
The discussion focuses on performance/operability implications for local or constrained environments where full in-memory weight residency is not possible.
It provides an empirical/technical angle on how storage hierarchy behavior (SSD vs faster tiers) affects MoE inference practicality rather than treating it as a purely theoretical limitation.

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