biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Docs] add Dynamo/aibrix integration and kubeai/aks link (#32767)

Browse Source 
Signed-off-by: Paco Xu <paco.xu@daocloud.io>
This commit is contained in:
Paco Xu
2026-03-05 17:39:50 +08:00
committed by GitHub
parent ac773bbe80
commit 7493c51c55
5 changed files with 16 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
docs/deployment/integrations/aibrix.md Normal file
View File

@@ -0,0 +1,5 @@
# AIBrix
[AIBrix](https://github.com/vllm-project/aibrix) is a cloud-native control plane that integrates with vLLM to simplify Kubernetes deployment, scaling, routing, and LoRA adapter management for large language model inference.
For installation and usage instructions, please refer to the [AIBrix documentation](https://aibrix.readthedocs.io/).

7
docs/deployment/integrations/dynamo.md Normal file
View File

@@ -0,0 +1,7 @@
# NVIDIA Dynamo
[NVIDIA Dynamo](https://github.com/ai-dynamo/dynamo) is an open-source framework for distributed LLM inference that can run vLLM on Kubernetes with flexible serving architectures (e.g. aggregated/disaggregated, optional router/planner).
For Kubernetes deployment instructions and examples (including vLLM), see the [Deploying Dynamo on Kubernetes](https://github.com/ai-dynamo/dynamo/blob/main/docs/kubernetes/README.md) guide.
Background reading: InfoQ news coverage — [NVIDIA Dynamo simplifies Kubernetes deployment for LLM inference](https://www.infoq.com/news/2025/12/nvidia-dynamo-kubernetes/).

1
docs/deployment/integrations/kubeai.md
View File

@@ -5,6 +5,7 @@
Please see the Installation Guides for environment specific instructions:
- [Any Kubernetes Cluster](https://www.kubeai.org/installation/any/)
- [AKS](https://www.kubeai.org/installation/aks/)
- [EKS](https://www.kubeai.org/installation/eks/)
- [GKE](https://www.kubeai.org/installation/gke/)

3
docs/deployment/k8s.md
View File

@@ -11,6 +11,7 @@ Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine le
Alternatively, you can deploy vLLM to Kubernetes using any of the following:
- [Helm](frameworks/helm.md)
- [NVIDIA Dynamo](integrations/dynamo.md)
- [InftyAI/llmaz](integrations/llmaz.md)
- [llm-d](integrations/llm-d.md)
- [KAITO](integrations/kaito.md)
@@ -20,7 +21,7 @@ Alternatively, you can deploy vLLM to Kubernetes using any of the following:
- [kubernetes-sigs/lws](frameworks/lws.md)
- [meta-llama/llama-stack](integrations/llamastack.md)
- [substratusai/kubeai](integrations/kubeai.md)
- [vllm-project/aibrix](https://github.com/vllm-project/aibrix)
- [vllm-project/AIBrix](integrations/aibrix.md)
- [vllm-project/production-stack](integrations/production-stack.md)
## Deployment with CPUs