| title | subtitle |
|---|---|
Support Matrix |
Hardware, software, and build compatibility for Dynamo |
See also: Release Artifacts for container images, wheels, Helm charts, and crates | Feature Matrix for backend feature support
Latest stable release: v0.9.0 -- SGLang 0.5.8 | TensorRT-LLM 1.3.0rc1 | vLLM 0.14.1 | NIXL 0.9.0
| Requirement | Supported |
|---|---|
| GPU | NVIDIA Ampere, Ada Lovelace, Hopper, Blackwell |
| OS | Ubuntu 22.04, Ubuntu 24.04, CentOS Stream 9 (experimental) |
| Arch | x86_64, ARM64 (ARM64 requires Ubuntu 24.04) |
| CUDA 12 | Container images for SGLang and vLLM (CUDA 12.9) |
| CUDA 13 | Container images for TensorRT-LLM (CUDA 13.0); experimental for SGLang and vLLM in v0.8.x |
On this page: Backend Dependencies | CUDA and Drivers | Hardware | Platform | Cloud | Build Support
The following table shows the backend framework versions included with each Dynamo release:
| Dynamo | SGLang | TensorRT-LLM | vLLM | NIXL |
|---|---|---|---|---|
| main (ToT) | 0.5.9 |
1.3.0rc5 |
0.16.0 |
0.10.1 |
| v1.0.0 (in progress) | 0.5.9 |
1.3.0rc5 |
0.15.1 |
0.10.1 |
| v0.9.1 (in progress) | 0.5.8 |
1.3.0rc3 |
0.14.1 |
0.9.0 |
| v0.9.0 | 0.5.8 |
1.3.0rc1 |
0.14.1 |
0.9.0 |
| v0.8.1.post3 | 0.5.6.post2 |
1.2.0rc6.post3 |
0.12.0 |
0.8.0 |
| v0.8.1.post2 | 0.5.6.post2 |
1.2.0rc6.post2 |
0.12.0 |
0.8.0 |
| v0.8.1.post1 | 0.5.6.post2 |
1.2.0rc6.post1 |
0.12.0 |
0.8.0 |
| v0.8.1 | 0.5.6.post2 |
1.2.0rc6.post1 |
0.12.0 |
0.8.0 |
| v0.8.0 | 0.5.6.post2 |
1.2.0rc6.post1 |
0.12.0 |
0.8.0 |
| v0.7.1 | 0.5.4.post3 |
1.2.0rc3 |
0.11.0 |
0.8.0 |
| v0.7.0.post1 | 0.5.4.post3 |
1.2.0rc3 |
0.11.0 |
0.8.0 |
| v0.7.0 | 0.5.4.post3 |
1.2.0rc2 |
0.11.0 |
0.8.0 |
| v0.6.1.post1 | 0.5.3.post2 |
1.1.0rc5 |
0.11.0 |
0.6.0 |
| v0.6.1 | 0.5.3.post2 |
1.1.0rc5 |
0.11.0 |
0.6.0 |
| v0.6.0 | 0.5.3.post2 |
1.1.0rc5 |
0.11.0 |
0.6.0 |
- main (ToT) reflects the current development branch.
- Releases marked (in progress) or (planned) show target versions that may change before final release.
- Backend versions listed are the only versions tested and supported for each release.
- TensorRT-LLM does not support Python 3.11; installation of the
ai-dynamo[trtllm]wheel will fail on Python 3.11.
Dynamo container images include CUDA toolkit libraries. The host machine must have a compatible NVIDIA GPU driver installed.
| Dynamo Version | Backend | CUDA Toolkit | Min Driver | Notes |
|---|---|---|---|---|
| 1.0.0 (in progress) | SGLang | 12.9 | 575.xx+ | |
| 13.0 | 580.xx+ | |||
| TensorRT-LLM | 13.1 | 580.xx+ | ||
| vLLM | 12.9 | 575.xx+ | ||
| 13.0 | 580.xx+ | |||
| 0.9.1 (in progress) | SGLang | 12.9 | 575.xx+ | |
| TensorRT-LLM | 13.0 | 580.xx+ | ||
| vLLM | 12.9 | 575.xx+ | ||
| 0.9.0 | SGLang | 12.9 | 575.xx+ | |
| TensorRT-LLM | 13.0 | 580.xx+ | ||
| vLLM | 12.9 | 575.xx+ | ||
| 0.8.1 | SGLang | 12.9 | 575.xx+ | |
| 13.0 | 580.xx+ | Experimental | ||
| TensorRT-LLM | 13.0 | 580.xx+ | ||
| vLLM | 12.9 | 575.xx+ | ||
| 13.0 | 580.xx+ | Experimental | ||
| 0.8.0 | SGLang | 12.9 | 575.xx+ | |
| 13.0 | 580.xx+ | Experimental | ||
| TensorRT-LLM | 13.0 | 580.xx+ | ||
| vLLM | 12.9 | 575.xx+ | ||
| 13.0 | 580.xx+ | Experimental | ||
| 0.7.1 | SGLang | 12.8 | 570.xx+ | |
| TensorRT-LLM | 13.0 | 580.xx+ | ||
| vLLM | 12.9 | 575.xx+ | ||
| 0.7.0 | SGLang | 12.9 | 575.xx+ | |
| TensorRT-LLM | 13.0 | 580.xx+ | ||
| vLLM | 12.8 | 570.xx+ |
Patch versions (e.g., v0.8.1.post1, v0.7.0.post1) have the same CUDA support as their base version.
Experimental CUDA 13 images are not published for all versions. Check Release Artifacts for availability.
For detailed artifact versions and NGC links (including container images, Python wheels, Helm charts, and Rust crates), see the Release Artifacts page.
For detailed information on CUDA driver compatibility, forward compatibility, and troubleshooting:
- CUDA Compatibility Overview
- Why CUDA Compatibility
- Minor Version Compatibility
- Forward Compatibility
- FAQ
For extended driver compatibility beyond the minimum versions listed above, consider using cuda-compat packages on the host. See Forward Compatibility for details.
| CPU Architecture | Status |
|---|---|
| x86_64 | Supported |
| ARM64 | Supported |
Dynamo provides multi-arch container images supporting both AMD64 (x86_64) and ARM64 architectures. See Release Artifacts for available images.
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 |
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 validated on CentOS Stream 9 and Ubuntu (22.04, 24.04). Compatibility with other Linux distributions is expected but not officially verified.
Caution
KV Block Manager is supported only with Python 3.12. Python 3.12 support is currently limited to Ubuntu 24.04.
| Host Operating System | Version | Architecture | Status |
|---|---|---|---|
| Amazon Linux | 2023 | x86_64 | Supported |
Caution
AL2023 TensorRT-LLM Limitation: 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 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).
For version-specific artifact details, installation commands, and release history, see Release Artifacts.
Dynamo currently provides build support in the following ways:
-
Wheels: We distribute Python wheels of Dynamo and KV Block Manager:
- ai-dynamo
- ai-dynamo-runtime
- kvbm as a standalone implementation.
-
Dynamo Container Images: We distribute multi-arch images (x86 & ARM64 compatible) on NGC:
-
Helm Charts: NGC hosts the helm charts supporting Kubernetes deployments of Dynamo:
- Dynamo Platform (includes CRDs)
- Dynamo Graph
-
Rust Crates:
- dynamo-runtime
- dynamo-llm
- dynamo-async-openai
- dynamo-parsers
- dynamo-config (New in v0.8.0)
- dynamo-memory (New in v0.8.0)
Once you've confirmed that your platform and architecture are compatible, you can install Dynamo by following the Local Quick Start in the README.