BlendServe: New System Optimizes Offline Inference for Large Language Models Through Resource-Aware Batching
Researchers have developed BlendServe, a system that improves the efficiency of offline batch inference for large language models by balancing two competing optimization strategies: resource overlapping and prefix sharing. The system uses a resource-aware prefix tree to reorder requests with varying computational demands while maintaining high prefix sharing efficiency. BlendServe demonstrated up to 1.44× throughput improvements over industry-standard systems like vLLM and SGLang in testing.
BlendServe addresses a key challenge in offline batch inference for large language models: requests with diverse computational and memory requirements can be scheduled to maximize either resource overlapping or prefix sharing, but optimizing for one typically compromises the other. The system solves this by using a resource-aware prefix tree that intelligently reorders and overlaps requests with varied resource demands while preserving high prefix sharing benefits. The approach takes advantage of the relaxed latency requirements in offline inference scenarios, where applications are not time-sensitive. Evaluation on synthetic multi-modal workloads showed BlendServe achieving up to 1.44× throughput improvements compared to widely-used industry standards vLLM and SGLang. This work is relevant for cost-sensitive applications that process large batches of requests asynchronously.
What's missing
The paper's evaluation is limited to synthetic multi-modal workloads; real-world performance on diverse production workloads and the computational overhead of the resource-aware prefix tree scheduling algorithm itself are not discussed. Additionally, the paper does not provide detailed comparisons with other recent batching optimization approaches or discuss potential limitations of the approach.
What different sources said
- arXiv cs.LGCenter
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
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