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Release Artifacts

Dynamo Release Artifacts

This document provides a comprehensive inventory of all Dynamo release artifacts including container images, Python wheels, Helm charts, and Rust crates.

See also: Support Matrix for hardware and platform compatibility | Feature Matrix for backend feature support

Release history in this document begins at v0.6.0.

Current Release: Dynamo v0.8.1

Patch Release: v0.8.1.post1 (Jan 23, 2026)

v0.8.1.post1 is a patch release for PyPI wheels and TRT-LLM container only (no GitHub release). All other artifacts remain at v0.8.1.

Artifact Version Change Link
ai-dynamo 0.8.1.post1 Updated TRT-LLM to v1.2.0rc6.post2 PyPI
ai-dynamo-runtime 0.8.1.post1 Updated TRT-LLM to v1.2.0rc6.post2 PyPI
tensorrtllm-runtime 0.8.1.post1 TRT-LLM v1.2.0rc6.post2 NGC

Container Images

Image:Tag Description Backend CUDA Arch NGC Notes
vllm-runtime:0.8.1 Runtime container for vLLM backend vLLM v0.12.0 v12.9 AMD64/ARM64 link
vllm-runtime:0.8.1-cuda13 Runtime container for vLLM backend (CUDA 13) vLLM v0.12.0 v13.0 AMD64/ARM64* — Fails to launch
sglang-runtime:0.8.1 Runtime container for SGLang backend SGLang v0.5.6.post2 v12.9 AMD64/ARM64 link
sglang-runtime:0.8.1-cuda13 Runtime container for SGLang backend (CUDA 13) SGLang v0.5.6.post2 v13.0 AMD64/ARM64* link Experimental
tensorrtllm-runtime:0.8.1 Runtime container for TensorRT-LLM backend TRT-LLM v1.2.0rc6.post1 v13.0 AMD64/ARM64 link
dynamo-frontend:0.8.1 API gateway with Endpoint Prediction Protocol (EPP) — — AMD64/ARM64 link
kubernetes-operator:0.8.1 Kubernetes operator for Dynamo deployments — — AMD64/ARM64 link

* Multimodal inference on CUDA 13 images: works on AMD64 for all backends; works on ARM64 only for TensorRT-LLM (vllm-runtime:*-cuda13 and sglang-runtime:*-cuda13 do not support multimodality on ARM64).

Python Wheels

We recommend using the TensorRT-LLM NGC container instead of the ai-dynamo[trtllm] wheel. See the NGC container collection for supported images.

Package Description Python Platform PyPI
ai-dynamo==0.8.1 Main package with backend integrations (vLLM, SGLang, TRT-LLM) 3.10–3.12 Linux (glibc v2.28+) link
ai-dynamo-runtime==0.8.1 Core Python bindings for Dynamo runtime 3.10–3.12 Linux (glibc v2.28+) link
kvbm==0.8.1 KV Block Manager for disaggregated KV cache 3.12 Linux (glibc v2.28+) link

Helm Charts

Chart Description NGC
dynamo-crds-0.8.1 Custom Resource Definitions for Dynamo Kubernetes resources link
dynamo-platform-0.8.1 Platform services (etcd, NATS) for Dynamo cluster link
dynamo-graph-0.8.1 Deployment graph controller for Dynamo workloads link

Rust Crates

Crate Description MSRV (Rust) crates.io
dynamo-runtime@0.8.1 Core distributed runtime library v1.82 link
dynamo-llm@0.8.1 LLM inference engine v1.82 link
dynamo-async-openai@0.8.1 Async OpenAI-compatible API client v1.82 link
dynamo-parsers@0.8.1 Protocol parsers (SSE, JSON streaming) v1.82 link
dynamo-memory@0.8.1 Memory management utilities v1.82 link
dynamo-config@0.8.1 Configuration management v1.82 link

Quick Install Commands

Container Images (NGC)

For detailed run instructions, see the Container README or backend-specific guides: vLLM | SGLang | TensorRT-LLM

# Runtime containers
docker pull nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.1
docker pull nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.8.1
docker pull nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.8.1.post1

# CUDA 13 variants (experimental)
# vLLM CUDA 13 image fails to launch (known issue)
# docker pull nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.1-cuda13
docker pull nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.8.1-cuda13

# Infrastructure containers
docker pull nvcr.io/nvidia/ai-dynamo/dynamo-frontend:0.8.1
docker pull nvcr.io/nvidia/ai-dynamo/kubernetes-operator:0.8.1

Python Wheels (PyPI)

For detailed installation instructions, see the Local Quick Start in the README.

# Install Dynamo with a specific backend (Recommended)
uv pip install "ai-dynamo[vllm]==0.8.1.post1"
uv pip install "ai-dynamo[sglang]==0.8.1.post1"
# TensorRT-LLM requires the NVIDIA PyPI index and pip
pip install --pre --extra-index-url https://pypi.nvidia.com "ai-dynamo[trtllm]==0.8.1.post1"

# Install Dynamo core only
uv pip install ai-dynamo==0.8.1.post1

# Install standalone KVBM (Python 3.12 only)
uv pip install kvbm==0.8.1

Helm Charts (NGC)

For Kubernetes deployment instructions, see the Kubernetes Installation Guide.

helm install dynamo-crds oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-crds --version 0.8.1
helm install dynamo-platform oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-platform --version 0.8.1
helm install dynamo-graph oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-graph --version 0.8.1

Rust Crates (crates.io)

For API documentation, see each crate on docs.rs. To build Dynamo from source, see Building from Source.

cargo add dynamo-runtime@0.8.1
cargo add dynamo-llm@0.8.1
cargo add dynamo-async-openai@0.8.1
cargo add dynamo-parsers@0.8.1
cargo add dynamo-memory@0.8.1
cargo add dynamo-config@0.8.1

CUDA and Driver Requirements

For detailed CUDA toolkit versions and minimum driver requirements for each container image, see the Support Matrix.

Known Issues

For a complete list of known issues, refer to the release notes for each patch:

Known Artifact Issues

Version Artifact Issue Status
v0.8.1 vllm-runtime:0.8.1-cuda13 Container fails to launch. Known issue
v0.8.1 sglang-runtime:0.8.1-cuda13, vllm-runtime:0.8.1-cuda13 Multimodality not expected to work on ARM64. Works on AMD64. Known limitation
v0.8.0 sglang-runtime:0.8.0-cuda13 CuDNN installation issue caused PyTorch v2.9.1 compatibility problems with nn.Conv3d, resulting in performance degradation and excessive memory usage in multimodal workloads. Fixed in v0.8.1 (#5461)

Release History

  • v0.8.1.post1 Patch: Updated TRT-LLM to v1.2.0rc6.post2 (PyPI wheels and TRT-LLM container only)
  • Standalone Frontend Container: dynamo-frontend added in v0.8.0
  • CUDA 13 Runtimes: Experimental CUDA 13 runtime for vLLM and SGLang in v0.8.0
  • New Rust Crates: dynamo-memory and dynamo-config added in v0.8.0

GitHub Releases

Version Release Date GitHub Docs
v0.8.1 Jan 23, 2026 Release Docs
v0.8.0 Jan 15, 2026 Release Docs
v0.7.1 Dec 15, 2025 Release GitHub
v0.7.0 Nov 26, 2025 Release GitHub
v0.6.1 Nov 6, 2025 Release GitHub
v0.6.0 Oct 28, 2025 Release GitHub

Container Images

NGC Collection: ai-dynamo

To access a specific version, append ?version=TAG to the container URL: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/{container}?version={tag}

vllm-runtime

Image:Tag vLLM Arch CUDA Notes
vllm-runtime:0.8.1 v0.12.0 AMD64/ARM64 v12.9
vllm-runtime:0.8.0 v0.12.0 AMD64/ARM64 v12.9
vllm-runtime:0.8.0-cuda13 v0.12.0 AMD64/ARM64 v13.0 Experimental
vllm-runtime:0.7.0.post2 v0.11.2 AMD64/ARM64 v12.8 Patch
vllm-runtime:0.7.1 v0.11.0 AMD64/ARM64 v12.8
vllm-runtime:0.7.0.post1 v0.11.0 AMD64/ARM64 v12.8 Patch
vllm-runtime:0.7.0 v0.11.0 AMD64/ARM64 v12.8
vllm-runtime:0.6.1.post1 v0.11.0 AMD64/ARM64 v12.8 Patch
vllm-runtime:0.6.1 v0.11.0 AMD64/ARM64 v12.8
vllm-runtime:0.6.0 v0.11.0 AMD64 v12.8

sglang-runtime

Image:Tag SGLang Arch CUDA Notes
sglang-runtime:0.8.1 v0.5.6.post2 AMD64/ARM64 v12.9
sglang-runtime:0.8.1-cuda13 v0.5.6.post2 AMD64/ARM64 v13.0 Experimental
sglang-runtime:0.8.0 v0.5.6.post2 AMD64/ARM64 v12.9
sglang-runtime:0.8.0-cuda13 v0.5.6.post2 AMD64/ARM64 v13.0 Experimental
sglang-runtime:0.7.1 v0.5.4.post3 AMD64/ARM64 v12.9
sglang-runtime:0.7.0.post1 v0.5.4.post3 AMD64/ARM64 v12.9 Patch
sglang-runtime:0.7.0 v0.5.4.post3 AMD64/ARM64 v12.9
sglang-runtime:0.6.1.post1 v0.5.3.post2 AMD64/ARM64 v12.9 Patch
sglang-runtime:0.6.1 v0.5.3.post2 AMD64/ARM64 v12.9
sglang-runtime:0.6.0 v0.5.3.post2 AMD64 v12.8

tensorrtllm-runtime

Image:Tag TRT-LLM Arch CUDA Notes
tensorrtllm-runtime:0.8.1.post1 v1.2.0rc6.post2 AMD64/ARM64 v13.0 Patch
tensorrtllm-runtime:0.8.1 v1.2.0rc6.post1 AMD64/ARM64 v13.0
tensorrtllm-runtime:0.8.0 v1.2.0rc6.post1 AMD64/ARM64 v13.0
tensorrtllm-runtime:0.7.0.post2 v1.2.0rc2 AMD64/ARM64 v13.0 Patch
tensorrtllm-runtime:0.7.1 v1.2.0rc3 AMD64/ARM64 v13.0
tensorrtllm-runtime:0.7.0.post1 v1.2.0rc3 AMD64/ARM64 v13.0 Patch
tensorrtllm-runtime:0.7.0 v1.2.0rc2 AMD64/ARM64 v13.0
tensorrtllm-runtime:0.6.1-cuda13 v1.2.0rc1 AMD64/ARM64 v13.0 Experimental
tensorrtllm-runtime:0.6.1.post1 v1.1.0rc5 AMD64/ARM64 v12.9 Patch
tensorrtllm-runtime:0.6.1 v1.1.0rc5 AMD64/ARM64 v12.9
tensorrtllm-runtime:0.6.0 v1.1.0rc5 AMD64/ARM64 v12.9

dynamo-frontend

Image:Tag Arch Notes
dynamo-frontend:0.8.1 AMD64/ARM64
dynamo-frontend:0.8.0 AMD64/ARM64 Initial

kubernetes-operator

Image:Tag Arch Notes
kubernetes-operator:0.8.1 AMD64/ARM64
kubernetes-operator:0.8.0 AMD64/ARM64
kubernetes-operator:0.7.1 AMD64/ARM64
kubernetes-operator:0.7.0.post1 AMD64/ARM64 Patch
kubernetes-operator:0.7.0 AMD64/ARM64
kubernetes-operator:0.6.1 AMD64/ARM64
kubernetes-operator:0.6.0 AMD64/ARM64

Python Wheels

PyPI: ai-dynamo | ai-dynamo-runtime | kvbm

To access a specific version: https://pypi.org/project/{package}/{version}/

ai-dynamo (wheel)

Package Python Platform Notes
ai-dynamo==0.8.1.post1 3.10–3.12 Linux (glibc v2.28+) TRT-LLM v1.2.0rc6.post2
ai-dynamo==0.8.1 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo==0.8.0 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo==0.7.1 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo==0.7.0 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo==0.6.1 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo==0.6.0 3.10–3.12 Linux (glibc v2.28+)

ai-dynamo-runtime (wheel)

Package Python Platform Notes
ai-dynamo-runtime==0.8.1.post1 3.10–3.12 Linux (glibc v2.28+) TRT-LLM v1.2.0rc6.post2
ai-dynamo-runtime==0.8.1 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo-runtime==0.8.0 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo-runtime==0.7.1 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo-runtime==0.7.0 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo-runtime==0.6.1 3.10–3.12 Linux (glibc v2.28+)
ai-dynamo-runtime==0.6.0 3.10–3.12 Linux (glibc v2.28+)

kvbm (wheel)

Package Python Platform Notes
kvbm==0.8.1 3.12 Linux (glibc v2.28+)
kvbm==0.8.0 3.12 Linux (glibc v2.28+)
kvbm==0.7.1 3.12 Linux (glibc v2.28+)
kvbm==0.7.0 3.12 Linux (glibc v2.28+) Initial

Helm Charts

NGC Helm Registry: ai-dynamo

Direct download: https://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/{chart}-{version}.tgz

dynamo-crds (Helm chart)

Chart Notes
dynamo-crds-0.8.1
dynamo-crds-0.8.0
dynamo-crds-0.7.1
dynamo-crds-0.7.0
dynamo-crds-0.6.1
dynamo-crds-0.6.0

dynamo-platform (Helm chart)

Chart Notes
dynamo-platform-0.8.1
dynamo-platform-0.8.0
dynamo-platform-0.7.1
dynamo-platform-0.7.0
dynamo-platform-0.6.1
dynamo-platform-0.6.0

dynamo-graph (Helm chart)

Chart Notes
dynamo-graph-0.8.1
dynamo-graph-0.8.0
dynamo-graph-0.7.1
dynamo-graph-0.7.0
dynamo-graph-0.6.1
dynamo-graph-0.6.0

Rust Crates

crates.io: dynamo-runtime | dynamo-llm | dynamo-async-openai | dynamo-parsers | dynamo-memory | dynamo-config

To access a specific version: https://crates.io/crates/{crate}/{version}

dynamo-runtime (crate)

Crate MSRV (Rust) Notes
dynamo-runtime@0.8.1 v1.82
dynamo-runtime@0.8.0 v1.82
dynamo-runtime@0.7.1 v1.82
dynamo-runtime@0.7.0 v1.82
dynamo-runtime@0.6.1 v1.82
dynamo-runtime@0.6.0 v1.82

dynamo-llm (crate)

Crate MSRV (Rust) Notes
dynamo-llm@0.8.1 v1.82
dynamo-llm@0.8.0 v1.82
dynamo-llm@0.7.1 v1.82
dynamo-llm@0.7.0 v1.82
dynamo-llm@0.6.1 v1.82
dynamo-llm@0.6.0 v1.82

dynamo-async-openai (crate)

Crate MSRV (Rust) Notes
dynamo-async-openai@0.8.1 v1.82
dynamo-async-openai@0.8.0 v1.82
dynamo-async-openai@0.7.1 v1.82
dynamo-async-openai@0.7.0 v1.82
dynamo-async-openai@0.6.1 v1.82
dynamo-async-openai@0.6.0 v1.82

dynamo-parsers (crate)

Crate MSRV (Rust) Notes
dynamo-parsers@0.8.1 v1.82
dynamo-parsers@0.8.0 v1.82
dynamo-parsers@0.7.1 v1.82
dynamo-parsers@0.7.0 v1.82
dynamo-parsers@0.6.1 v1.82
dynamo-parsers@0.6.0 v1.82

dynamo-memory (crate)

Crate MSRV (Rust) Notes
dynamo-memory@0.8.1 v1.82
dynamo-memory@0.8.0 v1.82 Initial

dynamo-config (crate)

Crate MSRV (Rust) Notes
dynamo-config@0.8.1 v1.82
dynamo-config@0.8.0 v1.82 Initial