vllm / tests
AGENTS.md95k starsChanged 11 days agoApache-2.0View on GitHub
- Test real objects on the lightest real path. Construct vLLM objects with their real constructors; do not stand them in with SimpleNamespace or MagicMock, set private attributes, or patch methods to reach a state. Mock only external services. Pick the cheapest setup that exercises the change: a layer or kernel test does not need vllmrunner, and single-rank parallel state is enough when parallelism is irrelevant. A fix to user-visible behavior needs at least one regression test through LLM (vllmrunner)…
We split this AGENTS.md into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Show the failure before the fix: When the bug is reproducible, run the new test on the base commit (rebuild if the fix touches csrc/) and …
- Very specific: line 17
Never buy a green CI: Do not add flaky/reruns, retry loops, skip, xfail, lowered thresholds, wider atol/rtol, or deletions to make a test …
- Distinctive: line 23
Deterministic and exact by construction: Use greedy or seeded sampling.
≈ “Common” is an estimate: at least 1 in 5 of the 36,368 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
tests
- Test real objects on the lightest real path. Construct vLLM objects with
their real constructors; do not stand them in with
SimpleNamespaceorMagicMock, set private attributes, or patch methods to reach a state. Mock only external services. Pick the cheapest setup that exercises the change: a layer or kernel test does not needvllm_runner, and single-rank parallel state is enough when parallelism is irrelevant. A fix to user-visible behavior needs at least one regression test throughLLM(vllm_runner) orRemoteOpenAIServer, with real weights when loading is involved. A test that only assertsassert_called*or "no exception" guards nothing. - Show the failure before the fix. When the bug is reproducible, run the new
test on the base commit (rebuild if the fix touches
csrc/) and confirm it fails for the reported reason, then passes with the fix. Paste both commands and the relevant output in the PR; "fails before" without output is not evidence. - Never buy a green CI. Do not add
flaky/reruns, retry loops,skip,xfail, lowered thresholds, wideratol/rtol, or deletions to make a test pass. Find out whether the failure is caused by your change or also happens on main (.buildkite/scripts/rerun-test.sh,docs/contributing/ci/failures.md); the only exception isxfail(strict=True)linking an open issue for a bug on main. - Deterministic and exact by construction. Use greedy or seeded sampling.
Assert equality where the result is exact (a change that must not alter
output is compared against the code it replaces, not another reference), and
keep tolerances as tight as the math allows. vLLM is not batch invariant by default: batch size, chunked
prefill, prefix caching, backend, and TP size can flip greedy tokens, so
compare against a reference with
check_logprobs_close/check_embeddings_close, or requireVLLM_BATCH_INVARIANT=1for exact equality. Never synchronize with a fixedsleep; poll an explicit condition with a timeout, and force race orderings with events. - Make sure CI runs it. A test no step in
.buildkite/test_areas/*.yamlselects never runs, which looks like passing. Check that the step'scommandsand pytest markers (core_model,slow_test,distributed) select the test and itssource_file_dependenciescover the changed files; update the step if not.
Trending
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Who's talking about vllm-project/vllm
- Sam Witteveen“vLLM - Turbo Charge your LLM Inference”YouTube · Jul 7, 2023 · 20k views
- Red Hat“Optimize LLM inference with vLLM”YouTube · Jul 22, 2025 · 20k views
- GithubTrends“How vLLM Cuts GPU Memory Waste in Half #Shorts #PagedAttention #AIOptimization”YouTube · Mar 21, 2026 · 1k views
- Red Hat“[vLLM Office Hours #37] InferenceMAX & vLLM - November 13, 2025”YouTube · Nov 14, 2025 · 865 views
- safesynt“vLLM serves LLMs fast with PagedAttention”YouTube · Oct 3, 2026 · 486 views
The biggest 5 of 97 mentions linking to the repository that our crawl found. Each line is their own headline.
See all 97
Talked about on GitHub, YouTube, Hacker News, Bluesky, the web and Stack Overflow (97 times).
On GitHub · 28
- Jul 28, 2025Feature Request: GLM 4.5 MoE support — ggml-org/llama.cpp166 reactions · 5 comments
- Apr 17, 2025[rollout] feat: introduce vLLM AsyncLLM to support multi-turn rollout — verl-project/verl52 reactions · 42 comments
- Apr 1, 2024Ecosystem compatibility with numpy 2.0 — numpy/numpy42 reactions · 167 comments
- Sep 29, 2025Feature Request: DeepSeek V3.2-Exp support — ggml-org/llama.cpp35 reactions · 54 comments
- Jun 20, 2023Add support to vLLM inference engine - to possibly gain x10 speedup in inference — oobabooga/textgen33 reactions · 19 comments
23 more on GitHub
- Dec 26, 20252025 年我提了 500 个 Pull Requests — yihong0618/gitblog32 reactions · 9 comments
- Jul 26, 2024[Feature] Generation Inputs: input_embeds — sgl-project/sglang30 reactions · 13 comments
- Sep 2, 2025Feature Request: Apertus models support — ggml-org/llama.cpp29 reactions · 14 comments
- Feb 7, 2024Development Roadmap (Deprecated) — sgl-project/sglang25 reactions · 17 comments
- Jul 14, 2023vLLM Support — guidance-ai/guidance25 reactions · 5 comments
- Jan 12, 2026[roadmap] verl 26Q1 roadmap — verl-project/verl24 reactions · 13 comments
- Feb 9, 2026model: support GLM MoE DSA arch (NOTE: indexer is not yet supported) — ggml-org/llama.cpp23 reactions · 28 comments
- May 6, 2026Feature Request: MTP (Multi-Token Prediction) Speculative Decoding for Gemma 4 — jundot/omlx20 reactions · 3 comments
- Apr 19, 2024llama3-instruct models not stopping at stop token — ollama/ollama20 reactions · 47 comments
- Feb 17, 2026Feature Request: Add Voxtral Realtime — ggml-org/llama.cpp18 reactions · 2 comments
- Apr 14, 2025kernel: support slightly faster merge_state_v2 cuda kernel — sgl-project/sglang17 reactions · 3 comments
- Nov 22, 2024[Roadmap] veRL Development Roadmap — verl-project/verl17 reactions · 7 comments
- Mar 8, 2026vllm: 0.16.0 -> 0.24.0 — NixOS/nixpkgs16 reactions · 91 comments
- Apr 7, 2025Support MHA with chunked prefix cache for DeepSeek chunked prefill — sgl-project/sglang16 reactions · 7 comments
- Mar 23, 2025[Model] Adding Qwen3 and Qwen3MoE — sgl-project/sglang16 reactions · 14 comments
- Sep 16, 2025fix: Resolve response format corruption due to incorrect encoding — openai/harmony15 reactions · 17 comments
- Sep 2, 2026[recipe] feat: preview DeepSeek-V4 exact 0-diff true-on-policy RL on Megatron and vLLM — verl-project/verl14 reactions · 2 comments
- Apr 25, 2026Support SM120 — deepseek-ai/DeepGEMM11 reactions · 18 comments
- Mar 30, 2025Add DeepSeek V3/R1 shared experts fusion — sgl-project/sglang11 reactions · 14 comments
- Jun 15, 2026Add DFlash support — ikawrakow/ik_llama.cpp9 reactions · 3 comments
- Apr 26, 2026[Quantization] add humming quantization kernel — sgl-project/sglang9 reactions · 17 comments
- Mar 29, 2026feat(kv-cache): Add TurboQuant KV cache quantization (WIP) — sgl-project/sglang8 reactions · 5 comments
- Dec 18, 2025[Draft] [Preview] Support gfx1201 — ROCm/aiter8 reactions · 5 comments
On YouTube · 20
- Jul 7, 2023vLLM - Turbo Charge your LLM Inference — Sam Witteveen20k views · 62 comments
- Jul 22, 2025Optimize LLM inference with vLLM — Red Hat20k views · 6 comments
- Mar 21, 2026How vLLM Cuts GPU Memory Waste in Half #Shorts #PagedAttention #AIOptimization — GithubTrends1k views
- Nov 14, 2025[vLLM Office Hours #37] InferenceMAX & vLLM - November 13, 2025 — Red Hat865 views
- Oct 3, 2026vLLM serves LLMs fast with PagedAttention — safesynt486 views
15 more on YouTube
- Jun 24, 2023vllm-project/vllm - Gource visualisation — Gourcer411 views
- Oct 3, 2026GPT 6.1 Sol Got Cheaper. Why Speculative Decoding Can Slow Your vLLM Server — The Augmented Expert383 views · 3 comments
- Aug 5, 2026Stop Using Ollama! (Unless you see these vLLM Benchmarks) 🚀 — Optilatesays it tested it296 views
- Jul 29, 2026llama.cpp vs vLLM: Which Local LLM Engine Do You Need? — thousandmilesAI249 views · 4 comments
- Apr 15, 2026Still brute-forcing with Transformers? vllm engine tested — LLM inference throughput doubled — DevCoverysays it tested it182 views
- Aug 19, 2026vLLM: the LLM inference engine with 89,000+ GitHub stars — AI Signal174 views
- Aug 26, 2026vLLM v0.28.0: Major Performance Updates for LLM Inference — Love More112 views · 1 comment
- Aug 8, 2025GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine f... — GitHub Daily Trend AI Podcast75 views
- Sep 14, 2026vLLM now serves 200+ AI models on one open-source engine — AI Signal68 views
- Sep 22, 2026vLLM v0.30.0 Release: Major Performance Improvements — Love More59 views · 1 comment
- Aug 19, 2024GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine f... — GitHub Daily Trend AI Podcast51 views
- May 25, 2026vLLM Explained: Serve Local LLMs Without Guessing Your GPU Budget — Doramagic AI19 views
- Oct 5, 2026vLLM 0.31: Keep Model Weights Across Restarts - AI Daily Diff Oct 5 — aidailydiff11 views
- Sep 11, 2026vLLM 0.29.0 Release: Major Performance Improvements — Love More8 views · 1 comment
- Sep 22, 2026vLLM Security Alert: Patch cache_salt DoS in Version 0.29.0 — dailytechhackglobal3 views
On Hacker News · 15
- Aug 29, 2026vLLM v0.28.0108 points · 43 comments
- Apr 4, 2026vLLM introduces memory optimizations for long-context inference5 points
- Apr 7, 2026vLLM IR: A Functional Intermediate Representation for vLLM4 points
- Sep 9, 2026vLLM 0.29.0: Model Runner V2 is now the default3 points
- Sep 10, 2023Vllm: High-throughput and memory-efficient inference and serving engine for LLMs3 points
10 more on Hacker News
- Aug 6, 2023Vllm3 points
- Feb 25, 2026vLLM (high-throughput LLM serving engine)2 points
- Dec 17, 2023Easy, fast, and cheap LLM serving for everyone2 points
- Dec 11, 2023Official PR Reveals the Inference Code for Mixtral 8x7B2 points
- Jun 25, 2023VLLM2 points
- Sep 16, 2026vLLM: Jev-like mode for the DiffusionGemma model1 point
- Jan 22, 2026vLLM multi-turn conversations design1 point
- Jan 24, 2024VLLM Sacrifices Accuracy for Speed1 point
- Dec 15, 2023vllm1 point
- Dec 15, 2023Mixtral Expert Parallelism1 point
On Bluesky · 15
- Apr 13, 2025Vllm est un projet de serveur d'inférence pour vos LLM qui se veut performant et efficace en terme de gestion mémoire ⬇️… — @ponceto91.bsky.social17 likes and reposts
- Feb 2, 2025vLLM has been updated to 0.71 with the enhancement to DeepSeek models. github.com/vllm-project... — @sungkim.bsky.social15 likes and reposts · 1 reply
- Jun 24, 2026No uses Ollama si quieres usar IA local con buen rendimiento. No exprime tu GPU al máximo. Mejor usa vLLM: ✓ Mejor eficiencia ✓ Más… — @midu.dev10 likes and reposts · 2 replies
- Jan 31, 2025Paper: R.I.P.: Better Models by Survival of the Fittest Prompts ( arxiv.org/abs/2501.18578 ) They used: - Fairseq2 from FAIR for LLM… — @sungkim.bsky.social4 likes and reposts
- Oct 28, 2026⚡ Serve LLMs faster and cheaper with vLLM's PagedAttention, continuous batching and a drop-in OpenAI-compatible API. 🔗… — @prosperflowio.bsky.social3 likes and reposts
10 more on Bluesky
- May 27, 2025🔥 Hot Repo! 🔥 (100+ new stars) 📦 vllm-project / vllm ⭐ 48,168 (+100) 🗒 Python A high-throughput and memory-efficient inference and… — @github-trending.bsky.social3 likes and reposts
- Apr 12, 2025🚀 Skyrocketing! 🚀 (200+ new stars) 📦 vllm-project / vllm ⭐ 44,413 (+267) 🗒 Python A high-throughput and memory-efficient inference and… — @github-trending.bsky.social2 likes and reposts
- Jan 21, 2025🔥 Hot Repo! 🔥 (100+ new stars) 📦 vllm-project / vllm ⭐ 34,172 (+145) 🗒 Python A high-throughput and memory-efficient inference and… — @github-trending.bsky.social2 likes and reposts
- Mar 23, 2026https://github.com/vllm-project/vllm — @tom-doerr.bsky.social1 likes and repost
- Aug 14, 2025A high-throughput and memory-efficient inference and serving engine for LLMs — @midnattssol.bsky.social1 likes and repost
- Apr 30, 2025🔥 Hot Repo! 🔥 (100+ new stars) 📦 vllm-project / vllm ⭐ 46,215 (+128) 🗒 Python A high-throughput and memory-efficient inference and… — @github-trending.bsky.social1 likes and repost
- Mar 7, 2025🔥 Hot Repo! 🔥 (100+ new stars) 📦 vllm-project / vllm ⭐ 40,586 (+148) 🗒 Python A high-throughput and memory-efficient inference and… — @github-trending.bsky.social1 likes and repost
- Oct 18, 2024📦 vllm-project / vllm ⭐ 28,641 (+85) 🗒 Python A high-throughput and memory-efficient inference and serving engine for LLMs — @github-trending.bsky.social1 likes and repost
- Oct 2, 2024interessante esse vllm, parece ser mais performático em cpus do que o ollama github.com/vllm-project... — @gaucho.dev1 likes and repost
- Dec 12, 2023🚀 Skyrocketing! 🚀 (200+ new stars) 📦 vllm-project / vllm ⭐ 11,193 (+321) 🗒 Python A high-throughput and memory-efficient inference and… — @github-trending.bsky.social1 likes and repost · 1 reply
On the web · 10
- May 20, 2026vLLM in Production: Ranked Configuration Decisions, Failure Modes, and the Architecture That Makes Them Work — dev.to
- Jan 23, 2026Local LLMs V - Installing and running vLLM | Rough Notes — javiercancela.com
- Jan 4, 2026Zero to Hero with vLLM: A Practical Guide for High‑Throughput LLM Inference | martinuke0's Blog — martinuke0.github.io
- Nov 6, 2025🧑🚀 Choosing the Right Engine to Launch Your LLM (LM Studio, Ollama, and vLLM) | by astronaut27 — medium.com
- Aug 26, 2025vLLM on x86: Because Not Everyone Can Afford a GPU Cluster — dev.to
5 more on the web
- Apr 1, 2025Run your own AI at scale: Tuning vLLM for Superb LLM Deployment (Vol. 1) — petarzrinscak.substack.com
- Feb 28, 2025Benchmarking llm serving performance a comprehensive guide — medium.comsays it tested it
- Feb 26, 2025Practical Large Language Model Serving with vLLM on VertexAI — infocruncher.substack.com
- Mar 9, 2024Run your own local Copilot. This is more of a blog on what I am… | by Thameem Abbas — tabbas97.medium.com
- Aug 14, 2023Efficiently Serving Open Source LLMs | by Ryan Shrott | TDS Archive — medium.com
On Stack Overflow · 9
- Apr 16, 2024How to install pip install torch==2.1.2+cu118 in linux?5 votes · 2 answers
- Jul 2, 2024How to set eos_token_id in llama3 in HuggingFaceLLM?2 votes
- May 15, 2024How to Load an Already Instantiated Hugging Face Model into vLLM for Inference?1 vote
- May 12, 2026How to Evaluate my RAG System using Local / free tier API model0 votes
- Mar 14, 2025Max len error when using Huggingface model0 votes
4 more on Stack Overflow
- Apr 27, 2024How does one use vllm with pytorch 2.2.2 and python 3.11?0 votes · 1 answer
- Dec 13, 2023ImportError: libcudart.so.12: cannot open shared object file: No such file or directory0 votes
- Nov 20, 2023How to send data in batches to a LLM0 votes
- Jan 24, 2024OSError: libcudart.so.10.2: cannot open shared object file: No such file or directory-1 votes
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 28,693 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- rust/AGENTS.mdAGENTS.md
- check-api-compatSkill
- ci-fails-buildkiteSkill
- debug-imaSkill
- kernel-microbenchmarkSkill
- model-microbenchmarkSkill
- model-recipesSkill
- pr-checklistSkill
- transformers-upgradeSkill
- triton-kernel-writingSkill
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