FleetSieve: Decision-Critical Profiling for SLO-Aware LLM Fleet Configuration
Choosing tensor-parallel (TP) degrees and replica counts for an LLM serving fleet is difficult because performance is not monotonic in TP and the feasible choice can change with load. Exhaustive profiling resolves this uncertainty, but measures many configurations that do not affect the final resource allocation. We present FleetSieve, which selects measurements according to their expected effect on a resource-coupled, SLO-aware fleet decision. FleetSieve models capacity and tail latency jointly, compares conservative and optimistic allocations, and stops when their remaining decision gap is below a specified tolerance. On a fixed H100 measurement grid for a 31B-parameter open-weight mode...
| Authors | Huang Cheng, Scott Zhang, Aubert Li |
|---|---|
| Primary category | cs.LG |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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