Top Tweets for #DisaggregatedInference

over 1 year ago

Inference at scale is pushing the boundaries of what today’s systems can handle. One promising direction? #DisaggregatedInference — splitting the serving pipeline into distinct stages like prefill and decode — to unlock better performance across the throughput-interactivity spectrum. While the concept has gained traction and sparked a wave of open source experimentation, real-world deployments remain rare. Why? Because the optimization space is vast, and orchestrating system-level coordination is incredibly complex. 📄 In our latest #NVIDIAResearch, we present a comprehensive systematic study of disaggregated inference at scale, evaluating hundreds of thousands of design points across varied model sizes, traffic patterns, and hardware configurations. Key findings: 1️⃣ Disaggregation shines in prefill-heavy traffic patterns and with larger models. 2️⃣ Dynamic rate matching and elastic scaling are essential to achieving Pareto-optimal performance. 3️⃣ There's no one-size-fits-all architecture — workload-aware tuning is critical. Whether you're building production inference infrastructure or exploring #LLM optimization techniques, this work offers actionable insights to balance system throughput with low-latency responsiveness. 🔬 Read the paper and dive into the data — let’s shape the future of scalable #AI serving, together. 📗https://t.co/TRqywTK0Px

Top Tweets for #DisaggregatedInference

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