# vllm-project/guidellm

Evaluate and Enhance Your LLM Deployments for Real-World Inference Needs

Repository: https://github.com/vllm-project/guidellm
Canonical: https://ross.abutalabs.com/products/guidellm
Homepage: https://vllm-project.github.io/guidellm/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-08-26T18:16:40+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 95, longevity 59
- inputs: {"age_days": 826, "days_push": 7, "days_rel": 33, "gap_med": 21.5, "n_releases_24m": 17}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1547, forks 219 (observed 2026-08-28T04:05:01.542619+00:00)

## What it is
GuideLLM is an SLO-aware benchmarking and evaluation platform for LLM inference deployments, simulating real-world workloads against OpenAI-compatible and vLLM-native servers. It generates configurable traffic patterns, supports real and synthetic multimodal datasets, and produces detailed latency and token-level reports for capacity planning.

## Use cases
- benchmark my vllm server under production-like load
- measure ttft and inter-token latency distributions for an llm endpoint
- find the max request rate my llm deployment can handle before slo violations
- run load tests against an openai-compatible api
- plan capacity for llm inference servers
- evaluate llm serving performance with synthetic multimodal workloads

## When to choose
- you need SLO-driven latency statistics (TTFT, ITL, e2e) for an LLM server
- you want reproducible traffic sweeps to find safe operating ranges
- you benchmark OpenAI-compatible or vLLM-native endpoints

## When to avoid
- you need to benchmark non-LLM services or general web APIs
- you want model quality/accuracy evaluation rather than serving performance
- you need a GUI-based load testing tool

## Facets
- artifact type: cli-tool
- maturity: active
- function: benchmarking, load-testing, monitoring
- domain: large-language-models, machine-learning, performance, developer-tools
- platform: python, cli, cross-platform
- tags: llm-inference, slo, openai-compatible, vllm, capacity-planning, synthetic-data

## Member repositories
- vllm-project/guidellm (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:01.542619+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:31.056596+00:00, confidence not recorded.
  - readme: https://github.com/vllm-project/guidellm (fetched 2026-08-28T04:05:01.542619+00:00, sha 975f5d5a9a88)
  - homepage: https://vllm-project.github.io/guidellm/ (fetched 2026-08-29T11:31:32.773019+00:00, sha 0cc59130a56d)
  - registry_pypi: https://pypi.org/pypi/guidellm/json (fetched 2026-08-29T11:31:32.781777+00:00, sha 5a736b37a902)
- Data as of 2026-08-30T08:39:29.467469+00:00.