Bursty arrivals speed up LLM inference
When Bursty Traffic Makes LLM Inference FastereventJul 30, 2026 person Akira van de GroenendaalTL;DR: Burstier arrivals made LLM inference faster in my benchmarks, contradicting standard intuition—burstiness separates decode tokens from heavy prefills, reducing interference. However, that interference is largely attributable to a kernel optimization artifact, and the benefits of burstiness only appear when mixed batches are inefficient.IntroductionBursty workloads have been giving computer scientists headaches for decades, and modern LLM serving is no different. Big bursts create instantaneous load and latency spikes, and there’s a whole host of literature exploring how to deal with them.The reason burstiness hurts, intuitively, is because we have the same mean arrival rate but requests are more likely to arrive very close together, or very far apart.
- ▪When Bursty Traffic Makes LLM Inference FastereventJul 30, 2026 person Akira van de GroenendaalTL;DR: Burstier arrivals made LLM inference faster in my benchmarks, contradicting standard intuition—burstiness separates decode tokens from hea
- ▪However, that interference is largely attributable to a kernel optimization artifact, and the benefits of burstiness only appear when mixed batches are inefficient.IntroductionBursty workloads have been giving computer scientists headaches
- ▪Big bursts create instantaneous load and latency spikes, and there’s a whole host of literature exploring how to deal with them.The reason burstiness hurts, intuitively, is because we have the same mean arrival rate but requests are more li
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| Original publisher | Harvard Systems Group |
| Canonical URL | https://systems.seas.harvard.edu/blog/burstiness-is-all-you-need/ |
| Publication time | Fri, 31 Jul 2026 18:35:24 +0000 |
| Retrieval time | 2026-07-31T18:49:01.182Z |
| Last seen | 2026-07-31T18:49:01.182Z |
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When Bursty Traffic Makes LLM Inference FastereventJul 30, 2026 person Akira van de GroenendaalTL;DR: Burstier arrivals made LLM inference faster in my benchmarks, contradicting standard intuition—burstiness separates decode tokens from heavy prefills, reducing interference. However, that interference is largely attributable to a kernel optimization artifact, and the benefits of burstiness only appear when mixed batches are inefficient.IntroductionBursty workloads have been giving computer scientists headaches for decades, and modern LLM serving is no different.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Harvard Systems Group.
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