We host regular meetups around the world. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights.
## Upcoming Meetups
Stay tuned for upcoming meetups! Follow us on [Twitter/X](https://x.com/vllm_project), join our [Slack](https://slack.vllm.ai), and follow vLLM on [Luma](https://luma.com/vLLM-Meetups) to get notified about new events.
## Past Meetups
Below you'll find slides and recordings from our previous meetups:
-[vLLM Bangkok Meetup](https://luma.com/v0f647nv), November 21st 2025. [[Slides]](https://drive.google.com/drive/folders/1H0DS57F8HQ5q3kSOSoRmucPJWL3E0A_X?usp=sharing)
-[vLLM Zurich Meetup](https://luma.com/0gls27kb), November 6th 2025. [[Slides]](https://docs.google.com/presentation/d/1UC9PTLCHYXQpOmJDSFg6Sljra3iVXzc09DeEI7dnxMc/edit?usp=sharing)[[Recording]](https://www.youtube.com/watch?v=6m6ZE6yVEDI)
-[vLLM Beijing Meetup](https://mp.weixin.qq.com/s/xSrYXjNgr1HbCP4ExYNG1w), November 1st 2025. [[Slides]](https://drive.google.com/drive/folders/1nQJ8ZkLSjKxvu36sSHaceVXtttbLvvu-?usp=drive_link)
-[vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/__xb4OyOsImz-9eAVrdlcg), October 25th 2025. [[Slides]](https://drive.google.com/drive/folders/1KqwjsFJLfEsC8wlDugnrR61zsWHt94Q6)
-[vLLM Toronto Meetup](https://luma.com/e80e0ymm), September 25th 2025. [[Slides]](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing)
-[vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ), August 30th 2025. [[Slides]](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA)
-[vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet), August 27th 2025. [[Slides]](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing)
-[vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg), August 23rd 2025. [[Slides]](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH)
-[vLLM Korea Meetup](https://luma.com/cgcgprmh), August 19th 2025. [[Slides]](https://drive.google.com/file/d/1bcrrAE1rxUgx0mjIeOWT6hNe2RefC5Hm/view).
-[vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA), August 2nd 2025. [[Slides]](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF)[[Recording]](https://www.chaspark.com/#/live/1166916873711665152).
-[NYC vLLM Meetup](https://lu.ma/c1rqyf1f), May 7th, 2025. [[Slides]](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing)
-[Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day), April 3rd 2025. [[Slides]](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
-[vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama), March 27th 2025. [[Slides]](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
-[The first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg), March 16th 2025. [[Slides]](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
-[The East Coast vLLM Meetup](https://lu.ma/7mu4k4xx), March 11th 2025. [[Slides]](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0)
-[The ninth vLLM meetup](https://lu.ma/h7g3kuj9), with Meta, February 27th 2025. [[Slides]](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing)
-[The eighth vLLM meetup](https://lu.ma/zep56hui), with Google Cloud, January 22nd 2025. [[Slides]](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing)
-[The seventh vLLM meetup](https://lu.ma/h0qvrajz), with Snowflake, November 14th 2024. [[Slides]](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing)
-[The sixth vLLM meetup](https://lu.ma/87q3nvnh), with NVIDIA, September 9th 2024. [[Slides]](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing)
-[The fifth vLLM meetup](https://lu.ma/lp0gyjqr), with AWS, July 24th 2024. [[Slides]](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing)
-[The fourth vLLM meetup](https://lu.ma/agivllm), with Cloudflare and BentoML, June 11th 2024. [[Slides]](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing)
-[The third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/), with Roblox, April 2nd 2024. [[Slides]](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing)
-[The second vLLM meetup](https://lu.ma/ygxbpzhl), with IBM Research, January 31st 2024. [[Slides]](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing)[[Video (vLLM Update)]](https://youtu.be/Y0C-DUvEnZQ) [[Video (IBM Research & torch.compile)]](https://youtu.be/m0dMtFLI-dg)
-[The first vLLM meetup](https://lu.ma/first-vllm-meetup), with a16z, October 5th 2023. [[Slides]](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing)
## Get Involved
**Want to host or speak at a vLLM meetup?** We're always looking for speakers and sponsors for our meetups. Whether you want to:
- Share your vLLM feature, use case, project extension, or deployment experience
- Host a meetup in your city
- Sponsor an event
Please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu).
Please visit [vllm.ai/events](https://vllm.ai/events) to learn more.
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
<!-- Note: Please sort them in alphabetical order. -->
<!-- Note: Please keep these consistent with README.md. -->
Cash Donations:
- a16z
- Dropbox
- Sequoia Capital
- Skywork AI
- ZhenFund
Compute Resources:
- Alibaba Cloud
- AMD
- Anyscale
- Arm
- AWS
- Crusoe Cloud
- Databricks
- DeepInfra
- Google Cloud
- IBM
- Intel
- Lambda Lab
- Nebius
- Novita AI
- NVIDIA
- Red Hat
- Replicate
- Roblox
- RunPod
- Trainy
- UC Berkeley
- UC San Diego
- Volcengine
Slack Sponsor: Anyscale
We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
Please visit [vllm.ai/#sponsors](https://vllm.ai/#sponsors) to learn more.
@@ -118,7 +118,7 @@ To support a model with interleaving sliding windows, we need to take care of th
- Make sure the model's `config.json` contains `layer_types`.
- In the modeling code, parse the correct sliding window value for every layer, and pass it to the attention layer's `per_layer_sliding_window` argument. For reference, check [this line](https://github.com/vllm-project/vllm/blob/996357e4808ca5eab97d4c97c7d25b3073f46aab/vllm/model_executor/models/llama.py#L171).
With these two steps, interleave sliding windows should work with the model.
With these two steps, interleaved sliding windows should work with the model.
### How to support models that use Mamba?
...
...
@@ -142,7 +142,7 @@ We use "mamba-like" to refer to layers that posses a state that is updated in-pl
For implementing new custom mamba-like layers, one should inherit from `MambaBase` and implement the methods `get_state_dtype`, `get_state_shape` to calculate the data types and state shapes at runtime, as well as `mamba_type` and `get_attn_backend`.
It is also necessary to implement the "attention meta-data" class which handles the meta-data that is common across all layers.
Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](../../../vllm/v1/attention/backends/short_conv_attn.py) for examples of this.
It is also worth noting that we should update `MAMBA_TYPE_TO_BACKEND_MAP` and `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/attention/backends/registry.py) when adding a new mamba backend.
It is also worth noting that we should update `MAMBA_TYPE_TO_BACKEND_MAP` and `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/v1/attention/backends/registry.py) when adding a new mamba backend.
Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.
# We need first call /start_profile api to start profile.
$ curl -X POST http://localhost:8000/start_profile
# Call model generate.
curl -X POST http://localhost:8000/v1/chat/completions \
-H"Content-Type: application/json"\
-d'{
"model": "meta-llama/Llama-3.1-8B-Instruct",
"messages": [
{
"role": "user",
"content": "San Francisco is a"
}
]
}'
# After need call /stop_profile api to stop profile.
$ curl -X POST http://localhost:8000/stop_profile
```
## Profile with NVIDIA Nsight Systems
Nsight systems is an advanced tool that exposes more profiling details, such as register and shared memory usage, annotated code regions and low-level CUDA APIs and events.
If you are using Podman instead of Docker, you might need to disable SELinux labeling by
adding `--security-opt label=disable` when running `podman build` command to avoid certain [existing issues](https://github.com/containers/buildah/discussions/4184).
!!! note
If you have not changed any C++ or CUDA kernel code, you can use precompiled wheels to significantly reduce Docker build time.
* **Enable the feature** by adding the build argument: `--build-arg VLLM_USE_PRECOMPILED="1"`.
* **How it works**: By default, vLLM automatically finds the correct wheels from our [Nightly Builds](../contributing/ci/nightly_builds.md) by using the merge-base commit with the upstream `main` branch.
* **Override commit**: To use wheels from a specific commit, provide the `--build-arg VLLM_PRECOMPILED_WHEEL_COMMIT=<commit_hash>` argument.
For a detailed explanation, refer to the documentation on 'Set up using Python-only build (without compilation)' part in [Build wheel from source](../contributing/ci/nightly_builds.md#precompiled-wheels-usage), these args are similar.
## Building for Arm64/aarch64
A docker container can be built for aarch64 systems such as the Nvidia Grace-Hopper and Grace-Blackwell. Using the flag `--platform "linux/arm64"` will build for arm64.
@@ -59,7 +59,7 @@ Then, run the following code to deploy it to the cloud:
cerebrium deploy
```
If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case`/run`)
If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case`/run`)
vLLM can be deployed with [KServe](https://github.com/kserve/kserve) on Kubernetes for highly scalable distributed model serving.
Please see [this guide](https://kserve.github.io/website/docs/model-serving/generative-inference/overview)for more details on using vLLM with KServe.
You can use vLLM with KServe's [Hugging Face serving runtime](https://kserve.github.io/website/docs/model-serving/generative-inference/overview) or via [`LLMInferenceService` that uses llm-d](https://kserve.github.io/website/docs/model-serving/generative-inference/llmisvc/llmisvc-overview).
vLLM can be deployed with [llm-d](https://github.com/llm-d/llm-d), a Kubernetes-native distributed inference serving stack providing well-lit paths for anyone to serve large generative AI models at scale. It helps achieve the fastest "time to state-of-the-art (SOTA) performance" for key OSS models across most hardware accelerators and infrastructure providers.
You can use vLLM with llm-d directly by following [this guide](https://llm-d.ai/docs/guide) or via [KServe's LLMInferenceService](https://kserve.github.io/website/docs/model-serving/generative-inference/llmisvc/llmisvc-overview).
@@ -10,7 +10,7 @@ If you are new to Kubernetes, don't worry: in the vLLM production stack [repo](h
## Pre-requisite
Ensure that you have a running Kubernetes environment with GPU (you can follow [this tutorial](https://github.com/vllm-project/production-stack/blob/main/tutorials/00-install-kubernetes-env.md) to install a Kubernetes environment on a bare-medal GPU machine).
Ensure that you have a running Kubernetes environment with GPU (you can follow [this tutorial](https://github.com/vllm-project/production-stack/blob/main/tutorials/00-install-kubernetes-env.md) to install a Kubernetes environment on a bare-metal GPU machine).
@@ -149,7 +149,7 @@ The CUDA Graphs wrapper no longer manages the warm-up logic. The warm-up process
## CUDA Graphs Compatibility of Attention Backends
To signal the CUDA Graphs compatibility of the attention backends, we introduce a new enum type [AttentionCGSupport][vllm.v1.attention.backends.utils.AttentionCGSupport], which is an enum type that tracks the capability of the attention backend to support CUDA Graphs. The value is sorted in the order of the capability, i.e., `ALWAYS`> `UNIFORM_BATCH`> `UNIFORM_SINGLE_TOKEN_DECODE`> `NEVER`.
To signal the CUDA Graphs compatibility of the attention backends, we introduce a new enum type [AttentionCGSupport][vllm.v1.attention.backend.AttentionCGSupport], which is an enum type that tracks the capability of the attention backend to support CUDA Graphs. The value is sorted in the order of the capability, i.e., `ALWAYS`> `UNIFORM_BATCH`> `UNIFORM_SINGLE_TOKEN_DECODE`> `NEVER`.
