BlendServe: New System Optimizes Offline Inference for Large Language Models Through Resource-Aware Batching
Researchers have introduced BlendServe, a system designed to optimize offline batch inference for large autoregressive models by combining resource overlapping and prefix sharing through a resource-aware prefix tree. Offline batch inference is increasingly used for latency-insensitive AI applications, but existing approaches struggle to simultaneously maximize both resource utilization and prefix sharing. BlendServe addresses this conflict and demonstrates up to 1.44× throughput improvement over widely-used systems vLLM and SGLang on synthetic multi-modal workloads.
BlendServe is a newly proposed inference system targeting offline batch processing of large autoregressive models, where requests can be reordered without strict latency constraints. The core challenge it addresses is a fundamental tension between two performance strategies: resource overlapping, which interleaves requests with varied compute and memory demands to keep hardware busy, and prefix sharing, which reuses cached computation across requests with common input prefixes. Prior systems optimizing for one strategy tend to undermine the other, leaving throughput gains on the table. BlendServe resolves this by introducing a resource-aware prefix tree data structure that jointly accounts for both dimensions when scheduling requests. Evaluations on synthetic multi-modal workloads show throughput gains of up to 1.44× compared to vLLM and SGLang, two widely adopted industry-standard inference frameworks. The work is particularly relevant as AI models grow more diverse in modality and complexity, making request heterogeneity an increasingly important factor in serving efficiency. The paper was submitted to arXiv in November 2024 and updated in June 2026.
What's missing
The evaluation is conducted exclusively on synthetic multi-modal workloads; results on real-world production workloads are not reported, leaving open questions about practical generalizability. It is also unclear whether BlendServe has been tested at production scale or integrated into any deployed system.
What different sources said
- arXiv cs.LGCenter
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
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