[ryanisnan]

Why does the system work like that? Is the cache local, or on Claude's servers?

Why not store the prompt cache to disk when it goes cold for a certain period of time, and then when a long-lived, cold conversation gets re-initiated, you can re-hydrate the cache from disk. Purge the cached prompts from disk after X days of inactivity, and tell users they cannot resume conversations over X days without burning budget.

[jetbalsa]

The cache is on Antropics server, its like a freeze frame of the LLM inner workings at the time. the LLM can pick up directly from this save state. as you can guess this save state has bits of the underlying model, their secret sauce. so it cannot be saved locally...

[dicethrowaway1]

Maybe they could let users store an encrypted copy of the cache? Since the users wouldn't have Anthropic's keys, it wouldn't leak any information about the model (beyond perhaps its number of parameters judging by the size).

[jetbalsa]

I'm unsure of the sizes needed for prompt cache, but I suspect its several gigs in size (A percentage of the model weight size), how would the user upload this every time they started a resumed a old idle session, also are they going to save /every/ session you do this with?

[im3w1l]

A few gigs of disk is not that expensive. Imo they should allocate every paying user (at least) one disk cache slot that doesn't expire after any time. Use it for their most recent long chat (a very short question-answer that could easily be replayed shouldn't evict a long convo).

[_flux]

I don't know how large the cache is, but Gemini guessed that the quantized cache size for Gemini 2.5 Pro / Claude 4 with 1M context size could be 78 gigabytes. ChatGPT guessed even bigger numbers. If someone is able to deliver a more precise estimate, you're welcome to :-).

So it would probably be a quite a long transfer to perform in these cases, probably not very feasible to implement at scale.

[spunker540]

Whats lost on this thread is these caches are in very tight supply - they are literally on the GPUs running inference. the GPUs must load all the tokens in the conversation (expensive) and then continuing the conversation can leverage the GPU cache to avoid re-loading the full context up to that point. but obviously GPUs are in super tight supply, so if a thread has been dead for a while, they need to re-use the GPU for other customers.

[skissane]

They could let you nominate an S3 bucket (or Azure/GCP/etc equivalent). Instead of dropping data from the cache, they encrypt it and save it to the bucket; on a cache miss they check the bucket and try to reload from it. You pay for the bucket; you control the expiry time for it; if it costs too much you just turn it off.

[northern-lights]

Encryption can only ensure the confidentiality of a message from a non-trusted third party but when that non-trusted third party happens to be your own machine hosting Claude Code, then it is pointless. You can always dump the keys (from your memory) that were used to encrypt/decrypt the message and use it to reconstruct the model weights (from the dump of your memory).

[dicethrowaway1]

jetbalsa said that the cache is on Anthropic's server, so the encryption and decryption would be server-side. You'd never see the encryption key, Anthropic would just give you an encrypted dump of the cache that would otherwise live on its server, and then decrypt with their own key when you replay the copy.