We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
-[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)
@@ -5,26 +5,34 @@ vLLM is a community project. Our compute resources for development and testing a
<!-- 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:
- AMD
- Anyscale
- AWS
- Crusoe Cloud
- Databricks
- DeepInfra
- Dropbox
- Google Cloud
- Lambda Lab
- Nebius
- Novita AI
- NVIDIA
- Replicate
- Roblox
- RunPod
- Sequoia Capital
- Skywork AI
- Trainy
- UC Berkeley
- UC San Diego
- ZhenFund
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.
This document provides a high-level guide on integrating a [HuggingFace Transformers](https://github.com/huggingface/transformers) model into vLLM.
```{note}
The complexity of adding a new model depends heavily on the model's architecture.
The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.
However, for models that include new operators (e.g., a new attention mechanism), the process can be a bit more complex.
```
```{note}
By default, vLLM models do not support multi-modal inputs. To enable multi-modal support,
please follow [this guide](#enabling-multimodal-inputs) after implementing the model here.
```
```{tip}
If you are encountering issues while integrating your model into vLLM, feel free to open an issue on our [GitHub](https://github.com/vllm-project/vllm/issues) repository.
We will be happy to help you out!
```
## 0. Fork the vLLM repository
Start by forking our [GitHub] repository and then [build it from source](#build-from-source).
This gives you the ability to modify the codebase and test your model.
```{tip}
If you don't want to fork the repository and modify vLLM's codebase, please refer to the "Out-of-Tree Model Integration" section below.
```
This guide walks you through the steps to implement a basic vLLM model.
## 1. Bring your model code
Clone the PyTorch model code from the HuggingFace Transformers repository and put it into the <gh-dir:vllm/model_executor/models> directory.
For instance, vLLM's [OPT model](gh-file:vllm/model_executor/models/opt.py) was adapted from the HuggingFace's [modeling_opt.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py) file.
First, clone the PyTorch model code from the source repository.
For instance, vLLM's [OPT model](gh-file:vllm/model_executor/models/opt.py) was adapted from
When copying the model code, make sure to review and adhere to the code's copyright and licensing terms.
```
:::{warning}
Make sure to review and adhere to the original code's copyright and licensing terms!
:::
## 2. Make your code compatible with vLLM
...
...
@@ -81,7 +57,17 @@ class MyModelForCausalLM(nn.Module):
### Computation Code
Rewrite the {meth}`~torch.nn.Module.forward` method of your model to remove any unnecessary code, such as training-specific code. Modify the input parameters to treat `input_ids` and `positions` as flattened tensors with a single batch size dimension, without a max-sequence length dimension.
- Add a `get_input_embeddings` method inside `MyModel` module that returns the text embeddings given `input_ids`. This is equivalent to directly calling the text embedding layer, but provides a unified interface in case `MyModel` is used within a composite multimodal model.
- Rewrite the {meth}`~torch.nn.Module.forward` method of your model to remove any unnecessary code, such as training-specific code. Modify the input parameters to treat `input_ids` and `positions` as flattened tensors with a single batch size dimension, without a max-sequence length dimension.
```python
defforward(
...
...
@@ -94,10 +80,10 @@ def forward(
...
```
```{note}
:::{note}
Currently, vLLM supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.
If your model employs a different attention mechanism, you will need to implement a new attention layer in vLLM.
```
:::
For reference, check out our [Llama implementation](gh-file:vllm/model_executor/models/llama.py). vLLM already supports a large number of models. It is recommended to find a model similar to yours and adapt it to your model's architecture. Check out <gh-dir:vllm/model_executor/models> for more examples.
...
...
@@ -105,51 +91,35 @@ For reference, check out our [Llama implementation](gh-file:vllm/model_executor/
If your model is too large to fit into a single GPU, you can use tensor parallelism to manage it.
To do this, substitute your model's linear and embedding layers with their tensor-parallel versions.
For the embedding layer, you can simply replace {class}`torch.nn.Embedding` with {code}`VocabParallelEmbedding`. For the output LM head, you can use {code}`ParallelLMHead`.
For the embedding layer, you can simply replace {class}`torch.nn.Embedding` with `VocabParallelEmbedding`. For the output LM head, you can use `ParallelLMHead`.
When it comes to the linear layers, we provide the following options to parallelize them:
-{code}`ReplicatedLinear`: Replicates the inputs and weights across multiple GPUs. No memory saving.
-{code}`RowParallelLinear`: The input tensor is partitioned along the hidden dimension. The weight matrix is partitioned along the rows (input dimension). An *all-reduce* operation is performed after the matrix multiplication to reduce the results. Typically used for the second FFN layer and the output linear transformation of the attention layer.
-{code}`ColumnParallelLinear`: The input tensor is replicated. The weight matrix is partitioned along the columns (output dimension). The result is partitioned along the column dimension. Typically used for the first FFN layer and the separated QKV transformation of the attention layer in the original Transformer.
-{code}`MergedColumnParallelLinear`: Column-parallel linear that merges multiple {code}`ColumnParallelLinear` operators. Typically used for the first FFN layer with weighted activation functions (e.g., SiLU). This class handles the sharded weight loading logic of multiple weight matrices.
-{code}`QKVParallelLinear`: Parallel linear layer for the query, key, and value projections of the multi-head and grouped-query attention mechanisms. When number of key/value heads are less than the world size, this class replicates the key/value heads properly. This class handles the weight loading and replication of the weight matrices.
-`ReplicatedLinear`: Replicates the inputs and weights across multiple GPUs. No memory saving.
-`RowParallelLinear`: The input tensor is partitioned along the hidden dimension. The weight matrix is partitioned along the rows (input dimension). An *all-reduce* operation is performed after the matrix multiplication to reduce the results. Typically used for the second FFN layer and the output linear transformation of the attention layer.
-`ColumnParallelLinear`: The input tensor is replicated. The weight matrix is partitioned along the columns (output dimension). The result is partitioned along the column dimension. Typically used for the first FFN layer and the separated QKV transformation of the attention layer in the original Transformer.
-`MergedColumnParallelLinear`: Column-parallel linear that merges multiple `ColumnParallelLinear` operators. Typically used for the first FFN layer with weighted activation functions (e.g., SiLU). This class handles the sharded weight loading logic of multiple weight matrices.
-`QKVParallelLinear`: Parallel linear layer for the query, key, and value projections of the multi-head and grouped-query attention mechanisms. When number of key/value heads are less than the world size, this class replicates the key/value heads properly. This class handles the weight loading and replication of the weight matrices.
Note that all the linear layers above take {code}`linear_method` as an input. vLLM will set this parameter according to different quantization schemes to support weight quantization.
Note that all the linear layers above take `linear_method` as an input. vLLM will set this parameter according to different quantization schemes to support weight quantization.
## 4. Implement the weight loading logic
You now need to implement the {code}`load_weights` method in your {code}`*ForCausalLM` class.
This method should load the weights from the HuggingFace's checkpoint file and assign them to the corresponding layers in your model. Specifically, for {code}`MergedColumnParallelLinear` and {code}`QKVParallelLinear` layers, if the original model has separated weight matrices, you need to load the different parts separately.
You now need to implement the `load_weights` method in your `*ForCausalLM` class.
This method should load the weights from the HuggingFace's checkpoint file and assign them to the corresponding layers in your model. Specifically, for `MergedColumnParallelLinear` and `QKVParallelLinear` layers, if the original model has separated weight matrices, you need to load the different parts separately.
## 5. Register your model
Finally, register your {code}`*ForCausalLM` class to the {code}`_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py>.
See [this page](#new-model-registration) for instructions on how to register your new model to be used by vLLM.
## 6. Out-of-Tree Model Integration
## Frequently Asked Questions
You can integrate a model without modifying the vLLM codebase. Steps 2, 3, and 4 are still required, but you can skip steps 1 and 5. Instead, write a plugin to register your model. For general introduction of the plugin system, see [plugin-system](#plugin-system).
### How to support models with interleaving sliding windows?
To register the model, use the following code:
For models with interleaving sliding windows (e.g. `google/gemma-2-2b-it` and `mistralai/Ministral-8B-Instruct-2410`), the scheduler will treat the model as a full-attention model, i.e., kv-cache of all tokens will not be dropped. This is to make sure prefix caching works with these models. Sliding window only appears as a parameter to the attention kernel computation.
To support a model with interleaving sliding windows, we need to take care of the following details:
If your model imports modules that initialize CUDA, consider lazy-importing it to avoid errors like {code}`RuntimeError: Cannot re-initialize CUDA in forked subprocess`:
- Make sure [this line](https://github.com/vllm-project/vllm/blob/996357e4808ca5eab97d4c97c7d25b3073f46aab/vllm/config.py#L308) evaluates `has_interleaved_attention` to `True` for this model, and set `self.hf_text_config.interleaved_sliding_window` to the format of interleaving sliding windows the model can understand. Then, `self.hf_text_config.sliding_window` will be deleted, and the model will be treated as a full-attention model.
- 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).
If your model is a multimodal model, ensure the model class implements the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
Read more about that [here](#enabling-multimodal-inputs).
```
```{note}
Although you can directly put these code snippets in your script using `vllm.LLM`, the recommended way is to place these snippets in a vLLM plugin. This ensures compatibility with various vLLM features like distributed inference and the API server.
```
With these two steps, interleave sliding windows should work with the model.
This document walks you through the steps to extend a basic model so that it accepts [multi-modal inputs](#multimodal-inputs).
## 1. Update the base vLLM model
It is assumed that you have already implemented the model in vLLM according to [these steps](#new-model-basic).
Further update the model as follows:
- Reserve a keyword parameter in {meth}`~torch.nn.Module.forward` for each input tensor that corresponds to a multi-modal input, as shown in the following example:
```diff
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
+ pixel_values: torch.Tensor,
) -> SamplerOutput:
```
More conveniently, you can simply pass `**kwargs` to the {meth}`~torch.nn.Module.forward` method and retrieve the keyword parameters for multimodal inputs from it.
- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings` that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.
The returned `multimodal_embeddings` must be either a **3D {class}`torch.Tensor`** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D {class}`torch.Tensor`'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request.
:::
- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings` to merge `multimodal_embeddings` with text embeddings from the `input_ids`. If input processing for the model is implemented correctly (see sections below), then you can leverage the utility function we provide to easily merge the embeddings.
vLLM relies on a model registry to determine how to run each model.
A list of pre-registered architectures can be found [here](#supported-models).
If your model is not on this list, you must register it to vLLM.
This page provides detailed instructions on how to do so.
## Built-in models
To add a model directly to the vLLM library, start by forking our [GitHub repository](https://github.com/vllm-project/vllm) and then [build it from source](#build-from-source).
This gives you the ability to modify the codebase and test your model.
After you have implemented your model (see [tutorial](#new-model-basic)), put it into the <gh-dir:vllm/model_executor/models> directory.
Then, add your model class to `_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py> so that it is automatically registered upon importing vLLM.
Finally, update our [list of supported models](#supported-models) to promote your model!
:::{important}
The list of models in each section should be maintained in alphabetical order.
:::
## Out-of-tree models
You can load an external model using a plugin without modifying the vLLM codebase.
If your model imports modules that initialize CUDA, consider lazy-importing it to avoid errors like `RuntimeError: Cannot re-initialize CUDA in forked subprocess`:
If your model is a multimodal model, ensure the model class implements the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
Read more about that [here](#supports-multimodal).
:::
:::{note}
Although you can directly put these code snippets in your script using `vllm.LLM`, the recommended way is to place these snippets in a vLLM plugin. This ensures compatibility with various vLLM features like distributed inference and the API server.
This page explains how to write unit tests to verify the implementation of your model.
## Required Tests
These tests are necessary to get your PR merged into vLLM library.
Without them, the CI for your PR will fail.
### Model loading
Include an example HuggingFace repository for your model in <gh-file:tests/models/registry.py>.
This enables a unit test that loads dummy weights to ensure that the model can be initialized in vLLM.
:::{important}
The list of models in each section should be maintained in alphabetical order.
:::
:::{tip}
If your model requires a development version of HF Transformers, you can set
`min_transformers_version` to skip the test in CI until the model is released.
:::
## Optional Tests
These tests are optional to get your PR merged into vLLM library.
Passing these tests provides more confidence that your implementation is correct, and helps avoid future regressions.
### Model correctness
These tests compare the model outputs of vLLM against [HF Transformers](https://github.com/huggingface/transformers). You can add new tests under the subdirectories of <gh-dir:tests/models>.
#### Generative models
For [generative models](#generative-models), there are two levels of correctness tests, as defined in <gh-file:tests/models/utils.py>:
- Exact correctness (`check_outputs_equal`): The text outputted by vLLM should exactly match the text outputted by HF.
- Logprobs similarity (`check_logprobs_close`): The logprobs outputted by vLLM should be in the top-k logprobs outputted by HF, and vice versa.
#### Pooling models
For [pooling models](#pooling-models), we simply check the cosine similarity, as defined in <gh-file:tests/models/embedding/utils.py>.
(mm-processing-tests)=
### Multi-modal processing
#### Common tests
Adding your model to <gh-file:tests/models/multimodal/processing/test_common.py> verifies that the following input combinations result in the same outputs:
- Text + multi-modal data
- Tokens + multi-modal data
- Text + cached multi-modal data
- Tokens + cached multi-modal data
#### Model-specific tests
You can add a new file under <gh-dir:tests/models/multimodal/processing> to run tests that only apply to your model.
For example, if the HF processor for your model accepts user-specified keyword arguments, you can verify that the keyword arguments are being applied correctly, such as in <gh-file:tests/models/multimodal/processing/test_phi3v.py>.
Currently, the repository does not pass the `mypy` tests.
```
# Contribution Guidelines
:::{note}
Currently, the repository is not fully checked by `mypy`.
:::
## Issues
If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
```{important}
:::{important}
If you discover a security vulnerability, please follow the instructions [here](gh-file:SECURITY.md#reporting-a-vulnerability).
```
:::
## Pull Requests & Code Reviews
...
...
@@ -81,16 +81,17 @@ appropriately to indicate the type of change. Please use one of the following:
-`[Misc]` for PRs that do not fit the above categories. Please use this
sparingly.
```{note}
:::{note}
If the PR spans more than one category, please include all relevant prefixes.
```
:::
### Code Quality
The PR needs to meet the following code quality standards:
- We adhere to [Google Python style guide](https://google.github.io/styleguide/pyguide.html) and [Google C++ style guide](https://google.github.io/styleguide/cppguide.html).
- Pass all linter checks. Please use <gh-file:format.sh> to format your code.
- Pass all linter checks. Please use `pre-commit` to format your code. See
<https://pre-commit.com/#usage> if `pre-commit` is new to you.
- The code needs to be well-documented to ensure future contributors can easily
understand the code.
- Include sufficient tests to ensure the project stays correct and robust. This
@@ -6,27 +6,27 @@ The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` en
When using `benchmarks/benchmark_serving.py`, you can enable profiling by passing the `--profile` flag.
```{warning}
:::{warning}
Only enable profiling in a development environment.
```
:::
Traces can be visualized using <https://ui.perfetto.dev/>.
```{tip}
:::{tip}
Only send a few requests through vLLM when profiling, as the traces can get quite large. Also, no need to untar the traces, they can be viewed directly.
```
:::
```{tip}
:::{tip}
To stop the profiler - it flushes out all the profile trace files to the directory. This takes time, for example for about 100 requests worth of data for a llama 70b, it takes about 10 minutes to flush out on a H100.
Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the server. Say something like 30 minutes.
`export VLLM_RPC_TIMEOUT=1800000`
```
:::
## Example commands and usage
### Offline Inference
Refer to <gh-file:examples/offline_inference_with_profiler.py> for an example.
Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example.
By default vLLM will build for all GPU types for widest distribution. If you are just building for the
current GPU type the machine is running on, you can add the argument `--build-arg torch_cuda_arch_list=""`
for vLLM to find the current GPU type and build for that.
```
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).
:::
## Building for Arm64/aarch64
A docker container can be built for aarch64 systems such as the Nvidia Grace-Hopper. At time of this writing, this requires the use
of PyTorch Nightly and should be considered **experimental**. Using the flag `--platform "linux/arm64"` will attempt to build for arm64.
```{note}
:::{note}
Multiple modules must be compiled, so this process can take a while. Recommend using `--build-arg max_jobs=` & `--build-arg nvcc_threads=`
flags to speed up build process. However, ensure your `max_jobs` is substantially larger than `nvcc_threads` to get the most benefits.
Keep an eye on memory usage with parallel jobs as it can be substantial (see example below).
```
:::
```console
#Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
...
...
@@ -76,6 +85,6 @@ $ docker run --runtime nvidia --gpus all \
The argument `vllm/vllm-openai` specifies the image to run, and should be replaced with the name of the custom-built image (the `-t` tag from the build command).
```{note}
:::{note}
**For version 0.4.1 and 0.4.2 only** - the vLLM docker images under these versions are supposed to be run under the root user since a library under the root user's home directory, i.e. `/root/.config/vllm/nccl/cu12/libnccl.so.2.18.1` is required to be loaded during runtime. If you are running the container under a different user, you may need to first change the permissions of the library (and all the parent directories) to allow the user to access it, then run vLLM with environment variable `VLLM_NCCL_SO_PATH=/root/.config/vllm/nccl/cu12/libnccl.so.2.18.1` .
[BentoML](https://github.com/bentoml/BentoML) allows you to deploy a large language model (LLM) server with vLLM as the backend, which exposes OpenAI-compatible endpoints. You can serve the model locally or containerize it as an OCI-complicant image and deploy it on Kubernetes.
[BentoML](https://github.com/bentoml/BentoML) allows you to deploy a large language model (LLM) server with vLLM as the backend, which exposes OpenAI-compatible endpoints. You can serve the model locally or containerize it as an OCI-compliant image and deploy it on Kubernetes.
For details, see the tutorial [vLLM inference in the BentoML documentation](https://docs.bentoml.com/en/latest/use-cases/large-language-models/vllm.html).
vLLM can be run on a cloud based GPU machine with [Cerebrium](https://www.cerebrium.ai/), a serverless AI infrastructure platform that makes it easier for companies to build and deploy AI based applications.
To install the Cerebrium client, run:
```console
$pip install cerebrium
$cerebrium login
pip install cerebrium
cerebrium login
```
Next, create your Cerebrium project, run:
```console
$cerebrium init vllm-project
cerebrium init vllm-project
```
Next, to install the required packages, add the following to your cerebrium.toml:
Next, let us add our code to handle inference for the LLM of your choice(`mistralai/Mistral-7B-Instruct-v0.1` for this example), add the following code to your main.py\`:
Next, let us add our code to handle inference for the LLM of your choice(`mistralai/Mistral-7B-Instruct-v0.1` for this example), add the following code to your `main.py`:
Then, run the following code to deploy it to the cloud
Then, run the following code to deploy it to the cloud:
```console
$cerebrium deploy
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 run on a cloud based GPU machine with [dstack](https://dstack.ai/), an open-source framework for running LLMs on any cloud. This tutorial assumes that you have already configured credentials, gateway, and GPU quotas on your cloud environment.
To install dstack client, run:
```console
$pip install"dstack[all]
$dstack server
pip install "dstack[all]
dstack server
```
Next, to configure your dstack project, run:
```console
$mkdir-p vllm-dstack
$cd vllm-dstack
$dstack init
mkdir -p vllm-dstack
cd vllm-dstack
dstack init
```
Next, to provision a VM instance with LLM of your choice(`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:
Next, to provision a VM instance with LLM of your choice(`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:
dstack automatically handles authentication on the gateway using dstack's tokens. Meanwhile, if you don't want to configure a gateway, you can provision dstack `Task` instead of `Service`. The `Task` is for development purpose only. If you want to know more about hands-on materials how to serve vLLM using dstack, check out [this repository](https://github.com/dstackai/dstack-examples/tree/main/deployment/vllm)
Helm is a package manager for Kubernetes. It will help you to deploy vLLM on k8s and automate the deployment of vLLMm Kubernetes applications. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variables values.
This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for helm install and documentation on architecture and values file.
## Prerequisites
Before you begin, ensure that you have the following:
- A running Kubernetes cluster
- NVIDIA Kubernetes Device Plugin (`k8s-device-plugin`): This can be found at [https://github.com/NVIDIA/k8s-device-plugin](https://github.com/NVIDIA/k8s-device-plugin)
- Available GPU resources in your cluster
- S3 with the model which will be deployed
## Installing the chart
To install the chart with the release name `test-vllm`:
