alt Hacker NewsDoesn't llama.cpp (or similar) have to evict the kv cache for this, so that performance is degraded when running multiple sessions? Or how do you load a model in memory and then use it in multiple sessions? I am still learning this stuff
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There are so many flags to llama.ccp that I won't try to say anything too strong, but I believe things related to `--kv-offload` mean you can have the KV cache in GPU VRAM, regular GPU RAM, paged to disk, etc...
I'm on a Mac with unified memory, so I can't easily benchmark it for you, but I think a PC with 64GB of regular RAM and a 24GB gaming card could swap between multiple sessions without too much pain. The weights could stay resident on the GPU.
On the other hand, I did just dump some Project Gutenberg texts into a prompt, and building that cache in the first place was slower than I though it would be.
The model is loaded once and can be used for multiple sessions, and even parallel requests.
llama.cpp uses a unified KV cache that is shared between requests (be they happening in parallel or not). As new requests come in, they'll evict no longer referenced branches, then move to evict the least recently used entry, and so on.
If you come back to a session that's been evicted it will just be parsed again. This is a problem only on very long context sessions, but it can still be a problem to you.
So one way to reduce such evictions (and reduce KV cache size significantly as a bonus) is to reduce the number of kv cache checkpoints.
Checkpoints allow you to branch a session at any point and not have to recompute it from the start. If you find that you rarely branch a conversation, or if you rely entirely on a coding harness, then setting ctx-checkpoints to 0 or 1 will save tons of VRAM and allow more different sessions to stay in VRAM. This is especially true for models with very large checkpoints (such as Gemma 4).