What is recorded here?
Split prompt prefills into uniform chunks and batched one compute-intensive chunk with pending token decodes, improving decode utilization and reducing pipeline imbalance.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Original SARATHI is not a claim to all later Sarathi-Serve schedulers. Decode-only and end-to-end speedups differ; workload, GPU, batching and latency constraints control benefit.
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People and systems
Amey Agrawal; Ashish Panwar; Jayashree Mohan; Nipun Kwatra; Bhargav S. Gulavani; Ramachandran Ramjee
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Agrawal et al.: chunked-prefill decode-maximal inference batching
Split prompt prefills into uniform chunks and batched one compute-intensive chunk with pending token decodes, improving decode utilization and reducing pipeline imbalance.
Scientific scope
Original SARATHI is not a claim to all later Sarathi-Serve schedulers. Decode-only and end-to-end speedups differ; workload, GPU, batching and latency constraints control benefit.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Amey Agrawal; Ashish Panwar; Jayashree Mohan; Nipun Kwatra; Bhargav S. Gulavani; Ramachandran Ramjee
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.