---
title: Dynamo Support Matrix
---
This document provides the support matrix for Dynamo, including hardware, software and build instructions.
> **See also:** [Feature Compatibility Matrix](/dynamo/v-0-8-0/getting-started/feature-matrix) for backend-specific feature support (vLLM, TensorRT-LLM, SGLang).
## Hardware Compatibility
| **CPU Architecture** | **Status** |
| :------------------- | :----------- |
| **x86_64** | Supported |
| **ARM64** | Supported |
### GPU Compatibility
If you are using a **GPU**, the following GPU models and architectures are supported:
| **GPU Architecture** | **Status** |
| :----------------------------------- | :--------- |
| **NVIDIA Blackwell Architecture** | Supported |
| **NVIDIA Hopper Architecture** | Supported |
| **NVIDIA Ada Lovelace Architecture** | Supported |
| **NVIDIA Ampere Architecture** | Supported |
## Platform Architecture Compatibility
**Dynamo** is compatible with the following platforms:
| **Operating System** | **Version** | **Architecture** | **Status** |
| :------------------- | :---------- | :--------------- | :----------- |
| **Ubuntu** | 22.04 | x86_64 | Supported |
| **Ubuntu** | 24.04 | x86_64 | Supported |
| **Ubuntu** | 24.04 | ARM64 | Supported |
| **CentOS Stream** | 9 | x86_64 | Experimental |
Wheels are built using a manylinux_2_28-compatible environment and they have been validated on CentOS 9 and Ubuntu (22.04, 24.04).
Compatibility with other Linux distributions is expected but has not been officially verified yet.
KV Block Manager is supported only with Python 3.12. Python 3.12 support is currently limited to Ubuntu 24.04.
## Software Compatibility
### Runtime Dependency
| **Python Package** | **Version** | glibc version | CUDA Version |
| :----------------- | :---------- | :------------------------------------ | :----------- |
| ai-dynamo | 0.8.0 | >=2.28 | |
| ai-dynamo-runtime | 0.8.0 | >=2.28 (Python 3.12 has known issues) | |
| NIXL | 0.8.0 | >=2.27 | >=11.8 |
### Build Dependency
The following table shows the dependency versions included with each Dynamo release:
| **Dependency** | **main (ToT)** | **v0.8.0** | **v0.7.1** | **v0.7.0.post1** | **v0.7.0** |
| :------------- | :------------- | :--------- | :--------- | :--------------- | :--------- |
| SGLang | 0.5.7 | 0.5.6.post2 | 0.5.3.post4| 0.5.3.post4 | 0.5.3.post4|
| TensorRT-LLM | 1.2.0rc6.post1 | 1.2.0rc6.post1 | 1.2.0rc3 | 1.2.0rc3 | 1.2.0rc2 |
| vLLM | 0.13.0 | 0.12.0 | 0.11.0 | 0.11.0 | 0.11.0 |
| NIXL | 0.8.0 | 0.8.0 | 0.8.0 | 0.8.0 | 0.8.0 |
**main (ToT)** reflects the current development branch.
Specific versions of TensorRT-LLM supported by Dynamo are subject to change. Currently TensorRT-LLM does not support Python 3.11 so installation of the ai-dynamo[trtllm] will fail.
### CUDA Support by Framework
| **Dynamo Version** | **SGLang** | **TensorRT-LLM** | **vLLM** |
| :------------------- | :-------------------------------- | :-----------------------| :-------------------------------- |
| **Dynamo 0.8.0** | CUDA 12.9, CUDA 13.0 (🧪) | CUDA 13.0 | CUDA 12.9, CUDA 13.0 (🧪) |
| **Dynamo 0.7.1** | CUDA 12.8 | CUDA 13.0 | CUDA 12.9 |
> 🧪 = Experimental
## Cloud Service Provider Compatibility
### AWS
| **Host Operating System** | **Version** | **Architecture** | **Status** |
| :------------------------ | :---------- | :--------------- | :--------- |
| **Amazon Linux** | 2023 | x86_64 | Supported¹ |
There is a known issue with the TensorRT-LLM framework when running the AL2023 container locally with `docker run --network host ...` due to a [bug](https://github.com/mpi4py/mpi4py/discussions/491#discussioncomment-12660609) in mpi4py. To avoid this issue, replace the `--network host` flag with more precise networking configuration by mapping only the necessary ports (e.g., 4222 for nats, 2379/2380 for etcd, 8000 for frontend).
## Build Support
**Dynamo** currently provides build support in the following ways:
- **Wheels**: We distribute Python wheels of Dynamo and KV Block Manager:
- [ai-dynamo](https://pypi.org/project/ai-dynamo/)
- [ai-dynamo-runtime](https://pypi.org/project/ai-dynamo-runtime/)
- **New as of Dynamo v0.7.0:** [kvbm](https://pypi.org/project/kvbm/) as a standalone implementation.
- **Dynamo Runtime Images**: We distribute multi-arch images (x86 & ARM64 compatible) of the Dynamo Runtime for each of the LLM inference frameworks on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo):
- [SGLang](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/sglang-runtime)
- [TensorRT-LLM](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/tensorrtllm-runtime)
- [vLLM](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/vllm-runtime)
- **Dynamo Kubernetes Operator Images**: We distribute multi-arch images (x86 & ARM64 compatible) of the Dynamo Operator on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo):
- [kubernetes-operator](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/kubernetes-operator) to simplify deployments of Dynamo Graphs.
- **Helm Charts**: [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo) hosts the helm charts supporting Kubernetes deployments of Dynamo:
- [Dynamo CRDs](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/helm-charts/dynamo-crds)
- [Dynamo Platform](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/helm-charts/dynamo-platform)
- [Dynamo Graph](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/helm-charts/dynamo-graph)
- **Rust Crates**:
- [dynamo-runtime](https://crates.io/crates/dynamo-runtime/)
- [dynamo-async-openai](https://crates.io/crates/dynamo-async-openai/)
- [dynamo-parsers](https://crates.io/crates/dynamo-parsers/)
- [dynamo-llm](https://crates.io/crates/dynamo-llm/)
Once you've confirmed that your platform and architecture are compatible, you can install **Dynamo** by following the instructions in the [Quick Start Guide](https://github.com/ai-dynamo/dynamo/blob/v0.8.0/README.md#installation).