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DriftSched improves LLM inference efficiency with adaptive scheduling

By PulseAugur Editorial · [2 sources] ·

Researchers have developed DriftSched, a framework to improve the efficiency of multi-tenant GPU inference for large language models. This system addresses the challenge of runtime token drift, where actual output lengths deviate from initial estimates, by using adaptive bias correction to reduce estimation errors by over 40%. Experiments showed that the Shortest-Job-First (SJF) scheduling policy, when combined with DriftSched, significantly reduced latency, with median end-to-end latency dropping by approximately 42%. The framework also includes a mechanism for runtime feedback-driven drift compensation and a benchmarking suite for evaluating QoS-aware scheduling on shared GPU infrastructure. AI

IMPACT Optimizes GPU resource utilization for LLM inference, potentially lowering costs and improving service responsiveness.

RANK_REASON The cluster contains a research paper detailing a new framework for LLM inference scheduling.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

DriftSched improves LLM inference efficiency with adaptive scheduling

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The cluster contains a research paper detailing a new framework for LLM inference scheduling.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 Deutsch(DE) · Kathiravan Palaniappan ·

    DriftSched: Adaptive QoS-Aware Scheduling under Runtime Token Drift for Multi-Tenant GPU Inference

    arXiv:2606.02982v1 Announce Type: cross Abstract: The rapid growth of large language model (LLM) inference services has increased the demand for efficient multi-tenant GPU scheduling. While modern inference runtimes such as vLLM improve throughput through continuous batching and …

  2. dev.to — LLM tag TIER_1 English(EN) · 云微 ·

    When CPU Noise Slows Down GPU Inference: Measuring Scheduler and IRQ Impact with eBPF

    <p>GPU inference often looks like a GPU problem, but the CPU still sits on the critical path. It prepares inputs, launches CUDA kernels, manages synchronization, handles runtime calls, and shares cores with system work, interrupts, and other tenants. If that CPU-side launch path …

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