[HN Gopher] ChunkLLM: A Lightweight Pluggable Framework for Acce...
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ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs
Inference
Author : PaulHoule
Score : 72 points
Date : 2025-10-24 11:41 UTC (11 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| toobulkeh wrote:
| High speed improvement (4x) with low quality loss (2%). Sounds
| promising.
| Vipsy wrote:
| Seeing frameworks like this pop up reminds me how much the LLM
| ecosystem is moving toward more modular and hardware-aware
| solutions. Performance at lower compute cost will be key as
| adoption spreads past tech giants. Curious to see how devs plug
| this into real-time apps; so much room for lightweight innovation
| now.
| djoldman wrote:
| From the results in Figure 5, it appears that this would only be
| advantageous for long long contexts.
|
| In particular, it is _slower_ when used with <30k token context.
| snowfield wrote:
| High context is pretty normal these days though, as you keep
| interfacing with the llms the context window just grows. And
| with mcps and RAG is trivial to get 30k contexts++ in every
| query
| Nav_Panel wrote:
| Love it, they're teaching LLMs how to skim texts properly, which
| is exactly the right approach for handling long contexts.
| ProofHouse wrote:
| wasn't this the attention sink concept to some degree? I mean
| it doesn't seem out of the realm of possibility that if the
| latency overhead isn't signifigant, that frontier models start
| adopting similar to DeepSeek OCR tech
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