KVServe delivers up to 9.13x job completion time speedup and 32.8x time-to-first-token reduction by making KV cache compression service-aware and adaptive in disaggregated LLM serving.
Muxserve: flexible spatial-temporal multi- plexing for multiple llm serving
4 Pith papers cite this work. Polarity classification is still indexing.
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JigsawRL achieves up to 1.85x higher throughput in LLM RL pipelines via pipeline multiplexing, sub-stage graphs, and look-ahead scheduling compared to prior systems.
Valve jointly bounds preemption latency and rate for online-offline LLM colocation on GPUs, delivering 34.6% higher cluster utilization and a 2,170-GPU saving in a production deployment of 8,054 GPUs with under 5% TTFT and 2% TPOT impact.
FlexPipe introduces runtime pipeline refactoring for LLMs to achieve higher resource efficiency and lower latency in serverless GPU clusters with fragmentation.
citing papers explorer
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KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving
KVServe delivers up to 9.13x job completion time speedup and 32.8x time-to-first-token reduction by making KV cache compression service-aware and adaptive in disaggregated LLM serving.
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JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training
JigsawRL achieves up to 1.85x higher throughput in LLM RL pipelines via pipeline multiplexing, sub-stage graphs, and look-ahead scheduling compared to prior systems.
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Valve: Production Online-Offline Inference Colocation with Jointly-Bounded Preemption Latency and Rate
Valve jointly bounds preemption latency and rate for online-offline LLM colocation on GPUs, delivering 34.6% higher cluster utilization and a 2,170-GPU saving in a production deployment of 8,054 GPUs with under 5% TTFT and 2% TPOT impact.
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FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters
FlexPipe introduces runtime pipeline refactoring for LLMs to achieve higher resource efficiency and lower latency in serverless GPU clusters with fragmentation.