Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving
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Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2608.06557 (cs)
[Submitted on 6 Aug 2026]
Title:Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving
Authors:Muhammad Adnan, Rohan Mahapatra, Prashant J. Nair, Daniel Berger, Pantea Zardoshti, Rodrigo Fonseca, Esha Choukse
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Abstract:The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests. LLM serving platforms today define response-latency service-level objectives, even though requests within the same service can differ by orders of magnitude in input length, generation length, execution cost, and the availability of reusable KV-cache state. As a result, requests governed by the same service level objective have different urgency: after accounting for the time required to execute them, some have substantial latency headroom while others have almost none. We define this headroom---the difference between a request's service level objective and its predicted remaining service time---as its per-request latency budget.
We present Cascade, an LLM serving system that estimates and continuously updates this budget from request characteristics, KV-cache state, and current system load. Unlike prior SLO-aware schedulers that use deadlines to govern request ordering alone, Cascade uses a single per-request budget to jointly coordinate request scheduling and KV-cache management across the memory hierarchy. Its scheduler prioritizes requests with little remaining budget, while its memory manager uses the same budget to decide whether non-resident KV state should be restored or prefetched from a deeper tier, retained in HBM, or recomputed. By directing queueing and data-movement overhead toward requests that can absorb it, Cascade improves SLO-satisfied goodput while preserving fairness across heterogeneous request classes. On production traces across three large language models, Cascade improves goodput by up to2.4x and reduces SLO violations by 40% relative to the default vLLM first-come, first-served scheduler.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.06557 [cs.DC] |
| (or arXiv:2608.06557v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2608.06557
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving, by Muhammad Adnan and 6 other authors
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