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Welcome to LLMServingSim

LLMServingSim is your sandbox for heterogeneous and disaggregated LLM serving infrastructure. Want to model a brand-new GPU? Sweep parallelism strategies? Throw exotic memory tiers (CXL, PIM) at a workload? Try a 32-GPU cluster you don't have? It's all within reach.

Setup is genuinely quick, clone, launch a container, compile, run. ~10 minutes. Once you're in, the simulator gets out of your way.

What you can do​

Configurable serving topologies

Configure TP / PP / EP / DP+EP across multiple instances, with chunked prefill, prefix caching, MoE routing, speculative decoding and memory offloading. Guides describe supported combinations and their limits.

Profiled compute, configurable memory

Measure NVIDIA GPU kernels with the vLLM-based profiler, reuse compatible latency bundles, and configure CPU, CXL and PIM memory tiers. Other accelerator backends require a compatible acquisition adapter.

Validated against vLLM

Sub-3% error across TTFT, TPOT, and latency statistics on the bundled Llama and Qwen references. These fixed-workload results do not establish accuracy for every model or deployment.

Wild scenarios welcome

ShareGPT traces, agentic sessions, 10× clusters, unreleased GPUs, exotic memory tiers, and model families beyond dense transformers — MoE, sparse attention, hybrid linear attention. Run experiments you cannot easily run on real hardware.

Three steps to your first simulation​

Prerequisites at a glance​

A Linux host with Docker is all you need to get started.

RequiredOptional but recommended
Linux (Ubuntu 22.04+ tested)NVIDIA GPU + Container Toolkit (profiling and new vLLM recordings)
DockerHugging Face token (for gated model configs)
Git with submodule support32 GB RAM (for the vLLM benchmark side)
~12 GB free disk

Already have everything? Jump straight to Simulator setup and run your first sim in 10 minutes.

Need a hand?​