Prefix Caching
PulseAugur coverage of Prefix Caching — every cluster mentioning Prefix Caching across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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LLM serving splits into two phases to double GPU throughput
Modern LLM serving architectures are evolving to handle requests more efficiently by splitting the process into two distinct phases: prefill and decode. The prefill phase, which processes the entire prompt, is compute-b…
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LLM inference engines optimize prompt processing with prefix caching
Large Language Models (LLMs) often recompute the same initial prompt tokens repeatedly, leading to inefficiency. This article explains that the KV cache, which stores intermediate states during token generation, is the …
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vLLM configurations impact LLM energy, performance, and accuracy
A new research paper investigates the trade-offs between energy consumption, performance, and accuracy when configuring inference engines like vLLM for large language models (LLMs). The study analyzed combinations of at…