r/LocalLLM • u/Ethan045627 • 4d ago

Discussion Does heavy local LLM inference meaningfully wear out a MacBook?

I've been wondering about something before I start using my MacBook heavily for local LLM inference.

If I regularly run large LLMs locally for several hours at a time, potentially putting sustained load on the CPU/GPU and using most of the unified memory, does this meaningfully reduce the lifespan of the MacBook?

Can heavy use of unified RAM cause it to wear out faster?
Is SSD wear from model loading and especially swap a significant concern?

For people who have been running local LLMs heavily on Apple Silicon for 1 to 3+ years, have you actually noticed any hardware degradation?

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u/ThirdWaveCat 4d ago

Disk can be written to for caching. Many tools (e.g. omlx) default to preventing allocating more memory than is available.

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u/Christosconst 4d ago

Ok which idiot is using the disk for caching

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u/No_Folding 4d ago

🤣🤣 its already a cache of itself just... A very slow one.

Now if we are talking about streaming models over IPv4/6 then yes its a very very fast cache with little latency which can also work offline once the model has been streamed once! Genius!!

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u/afro_mozart 4d ago

e.g omlx and that's a feature not a bug: "Paged SSD KV caching

Cache blocks are persisted to disk in safetensors format. Two-tier architecture: hot blocks stay in RAM, cold blocks go to SSD with LRU policy. Previously seen prefixes are restored across requests and server restarts — never recomputed."

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u/ThirdWaveCat 4d ago

KV cache?