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This tutorial details the validation of distributed Large Language Model serving benchmarks using NVIDIA's srt-slurm framework, focusing on converting declarative YAML configs into reproducible SLURM workflows. It demonstrates how to model a disaggregated prefill-and-decode deployment for specific models like DeepSeek-R1 and analyze performance trade-offs via Pareto frontiers in a simulated environment before production submission.

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Validating Distributed LLM Serving Benchmarks with NVIDIA srt-slurm, SLURM Recipes, Parameter Sweeps, and Pareto Analysis

MarkTechPostJuly 21, 20264 min read

In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for distributed LLM serving. We set up the project in Google Colab, inspect its internal architecture, define a cluster configuration, dry-run built-in and custom recipes, and model a disaggregated prefill-and-decode deployment […] The post Validating Distributed LLM Serving Benchmarks with NVIDIA srt-slurm, SLURM Recipes, Parameter Sweeps, and Pareto Analysis appeared first on MarkTechPost.

In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for distributed LLM serving. We set up the project in Google Colab, inspect its internal architecture, define a cluster configuration, dry-run built-in and custom recipes, and model a disaggregated prefill-and-decode deployment for DeepSeek-R1. We also generate parameter sweeps, interact…

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