KV Cache
topic on 3 shows · 16 statements across 11 episodes
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16 statements about KV Cache, every show
Movva: KV cache frequently exceeds model weight size
“You have to store a representation for every token that we sent through the language model. And it frequently Gets to be larger than the weights of the model themselves.”
Movva: KV cache memory usage is inefficient by 1-2 orders of magnitude
“Specifically, the KV cache is quite uncompressed right now. I think if you look at the entropy in a KV cache, it's nowhere near, it's not earning its keep. Like, we're storing many kilobytes of data in the KV cache per token. And that's probably off by an orde…”
Ultra-fast network interface cards could deliver 100x speedups in disaggregated AI inference
“If you were to somehow be able to, in like this theoretical dreamland, have extremely fast NICs, you could, in theory, spare that HBM and you could just transfer KVCache trans, like directly from one node to another. This would give you like almost a hundred X…”
KV cache compaction is the viable path to solve LLM continual learning
“If you're able to sort of make your KV almost infinite, and you're able to compact in such a way that you don't lose any of the knowledge. In that case, you can actually do a continual learning as you can actually solve continual learning. And this, as a, it's…”
Bubna: Transferring RL weights is fundamentally an OS memory problem
“Like the way you move around your KV cache and how efficiently you can do it, how efficiently you move your weights from your training GPUs to your inference GPUs in RL is, there's a lot of degrees of freedom, and it is basically a systems problem of Moving me…”
LLM prefill remains compute-bound while decoding phases are strictly memory-bound
“So prefill typically, and this changes as model architecture changes, prefill is right now compute bound. Most of the time. If the sequence is sufficiently long, it's compute bound on the decode side because you're doing a full pass over all the weights and th…”
Lenz: Local smartphone AI requires hybrid models due to KV cache limits
“So if you wanted to do something local on your phone to search your images, as an example, you can't do that without a hybrid architecture or without doing drastically changes because the model plus KVCache won't fit.”
Bachman: Manifest AI is releasing Power Retention architecture with fixed-size memory
“Power retention is the specific variant that we're about to release.
And it basically works by instead of the memory constantly growing, this constantly
Growing KVCache.
You have a memory that is a fixed size and each new token simply gets compressed into this…”
Bachman: Power Retention Delivers 100x Inference Speedup at 64k Context
“And at 64 K tokens, We get something like a 10 X speed up at training, but at inference time, because you're not only saving flops at inference time, but also paging in and out of memory of the KV cache, you actually get a hundred X speed ups from power retent…”
CPU latency in KV cache management bottlenecks GPU utilization
“Like your eviction policy runs on a CPU. Like that radix hashing algorithm and block hashing and all that stuff happens like primarily CPU. That's really important for performance because if you have latency in these steps, like you're not keeping your GPU uti…”
Beauchamp: DeepSeek slashes inference costs by shrinking KV cache
“What DeepSeek have achieved that's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cache significantly. And then by being able to do that, they're able to significantly reduce their inference costs.”
Patel: Reasoning models like OpenAI o1 increase compute costs by 50x
“When I do this with O-one, right, because it's doing that thinking phase of 10,000... It spends a lot of memory on generating this KV cache and reading this KV cache constantly. Now the maximum batch size, i.e. Concurrent users I can have, is a fraction of tha…”
Jamil: Writing in the Margins avoids re-prefilling tokens, halving compute cost
“Again, to the language model to generate the answer, and it would cost you another million, because the model has to reprocess this prefilling again of one million tokens, so it would cost you two million tokens, but with writing in the margins, it would cost …”
Writing in the Margins requires inference engine modifications but zero fine-tuning
“We are not doing any change to the model architecture, so you don't have to fine-tune anything, you don't have to change anything, like the can you use this stuff with, like, a Lank pane? No, because it requires a modification on how the inference engine is us…”
Masking out prior KV cache tokens pushes autoregressive transformers out of distribution
“The token number two in the KVCache is a contextualized version of the token zero, one, and two. So if you tell the model to only look at the last tokens you are creating an autoregressive model that is generating the logits of a P of let's say X, but only loo…”
Frankle: Needle in a Haystack eval fails to measure holistic context usage
“I think the problems with needle in a haystack are well known. You know, it doesn't measure anything real. You're not even testing the model's ability to holistically use the context just to identify one part of the context. So you can do some wacky things to …”