Paper, researchers, and primary source
Inference, evaluation & serving / inference_systems
vLLM introduces PagedAttention and a serving engine that manages key-value cache blocks like virtual memory so request batches waste less accelerator memory.
Contribution 1
Introduced PagedAttention for non-contiguous key-value cache storage.
Contribution 2
Enabled cache-block sharing across parallel samples and prompt prefixes.
Contribution 3
Demonstrated higher serving throughput under memory-bound online workloads.
Research context
inference_systems / 2023
Efficient Memory Management for Large Language Model Serving with PagedAttention places vllm inside the broader inference systems discussion at University of California, Berkeley, with pagedattention supplying a second analytical lens. The editorial sequence connects three claims: Introduced PagedAttention for non-contiguous key-value cache storage; Enabled cache-block sharing across parallel samples and prompt prefixes; and Demonstrated higher serving throughput under memory-bound online workloads. The combination matters because kv cache only has meaning under the paper's stated setup. Operationally, the record points to one consequence: cache management and continuous batching can raise serving capacity, but gains depend on request lengths, concurrency, hardware, model, and latency targets.
Methods and evidence reading
1 cataloged method notes
Method 1
The experimental design in Efficient Memory Management for Large Language Model Serving with PagedAttention tests introduced pagedattention for non-contiguous key-value cache storage and enabled cache-block sharing across parallel samples and prompt prefixes against the paper's documented baselines, datasets, model variants, or systems workloads.
How to read the evidence
For Efficient Memory Management for Large Language Model Serving with PagedAttention, the useful question is what the documented setup supports and where transfer remains untested. Its evidence chain begins with two documented moves: Introduced PagedAttention for non-contiguous key-value cache storage; and Enabled cache-block sharing across parallel samples and prompt prefixes. Reported evidence then addresses: Demonstrated higher serving throughput under memory-bound online workloads. The resulting interpretation is practical but conditional: cache management and continuous batching can raise serving capacity, but gains depend on request lengths, concurrency, hardware, model, and latency targets. Its boundary is that the empirical reach of Efficient Memory Management for Large Language Model Serving with PagedAttention stops at reported models, accelerator hardware, request shapes, numerical precision, service-level objectives, software revisions, and comparison baselines; broader pagedattention use therefore requires fresh measurements. Any extension should report how altered pagedattention conditions affect the original vllm result. To retest Efficient Memory Management for Large Language Model Serving with PagedAttention, hold the vllm baseline visible while changing pagedattention, then log where Enabled cache-block sharing across parallel samples and prompt prefixes no longer predicts the reported outcome.
Findings in the source record
1 paper-specific findings
- The reported evidence in Efficient Memory Management for Large Language Model Serving with PagedAttention supports demonstrated higher serving throughput under memory-bound online workloads.
Practical implication for AI builders
University of California, Berkeley / 2023
Proposed BrokenGPT application
Research blueprint / proposed status
Proposed route placement / vllm
For a proposed BrokenGPT experiment based on Efficient Memory Management for Large Language Model Serving with PagedAttention, replay BrokenGPT-shaped traffic through a pinned vLLM build and gate adoption on answer parity, cache utilization, time to first token, p95 latency, and cost. Keep the vllm path isolated, versioned, and attributable to this research record.
Proposed acceptance test / pagedattention
Validate the proposed vllm route against the paper's reported outcome: Demonstrated higher serving throughput under memory-bound online workloads. Measure inter-token delay, time to first token, goodput, and memory headroom for the Efficient Memory Management for Large Language Model Serving with PagedAttention candidate, then isolate pagedattention regressions before judging the proposed vllm route.
Proposed decision boundary / kv cache
Balance latency, throughput, and operational complexity before promoting the proposed kv cache design. Because any follow-on prototype should treat authentication, tokenization, failure recovery, workload drift, safety checks, and networking overhead as release gates around the paper's vllm hypothesis, adoption remains conditional on replay under BrokenGPT's selected model, runtime, and policy configuration.
Limitations, verification, and source
Boundaries recorded with the paper
Limitations
- The empirical reach of Efficient Memory Management for Large Language Model Serving with PagedAttention stops at reported models, accelerator hardware, request shapes, numerical precision, service-level objectives, software revisions, and comparison baselines; broader pagedattention use therefore requires fresh measurements.
- Any follow-on prototype should treat authentication, tokenization, failure recovery, workload drift, safety checks, and networking overhead as release gates around the paper's vllm hypothesis.
PRIMARY SOURCES
- 01Efficient Memory Management for Large Language Model Serving with PagedAttention
University of California, Berkeley — Primary primary arXiv paper / 12 September 2023 / Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, and 6 more