Comment by adonese
11 hours ago
Is there any place I can read about KV? Excuse my ignorance as I'm not familiar with this topic and I read scattered notes that deepseek's cost are well optimized due to how their kv cache work. But I want to read more how kv cache relates to the inference stack and where does it actually sit.
> AIUI, people are even experimenting with offloading the KV cache itself to storage, which may unlock this batching capability even beyond physical RAM limits as contexts grow.
Especially this point. Any reason that this idea was considered bad? Is it due to the speed difference between the GPU VRAM to the RAM?
KV cache generally grows linearly with your current context; it gets filled-in with your prompts during prompt processing, and newly created context gets tacked on during token generation. LLM inference uses it to semantically relate the currently-processed token to its pre-existing context.
> Any reason that this idea was considered bad?
Because the KV cache was too big, even for a small context. This is still an issue with open models other than DeepSeek V4, though to a somewhat smaller extent than used to be the case. But the tiny KV of DeepSeek V4 is genuinely new.