DynoSim: Simulating the Pareto Frontier
Quick Take
Tuning modern LLM deployments is complex due to interdependent choices like model backend and worker settings, which can shift bottlenecks unexpectedly. This complexity affects performance optimization across various configurations, particularly for larger models.
Key Points
- Deployment choices include model backend, tensor-parallel shape, and worker counts.
- Local improvements can inadvertently shift performance bottlenecks elsewhere.
- Larger models face heightened complexity in tuning and optimization.
- Autoscaling thresholds and KV cache behavior are critical tuning factors.
- Routing policy and scheduler settings further complicate deployment.
Article Excerpt
From source RSS / original summaryModern LLM serving is hard to tune because each deployment is a stack of interacting choices: model backend, tensor-parallel shape, prefill/decode split, worker... Modern LLM serving is hard to tune because each deployment is a stack of interacting choices: model backend, tensor-parallel shape, prefill/decode split, worker counts, scheduler settings, routing policy, KV cache behavior, autoscaling thresholds, and topology.
Those choices interact across layers, and a local improvement can shift the bottleneck somewhere else. For larger models… Source
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