base on A Datacenter Scale Distributed Inference Serving Framework <!-- SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->  [](https://opensource.org/licenses/Apache-2.0) [](https://github.com/ai-dynamo/dynamo/releases/latest) [](https://discord.gg/nvidia-dynamo) [](https://deepwiki.com/ai-dynamo/dynamo) | **[Roadmap](https://github.com/ai-dynamo/dynamo/issues/762)** | **[Documentation](https://docs.nvidia.com/dynamo/latest/index.html)** | **[Examples](https://github.com/ai-dynamo/examples)** | **[Design Proposals](https://github.com/ai-dynamo/enhancements)** | ### The Era of Multi-Node, Multi-GPU  Large language models are quickly outgrowing the memory and compute budget of any single GPU. Tensor-parallelism solves the capacity problem by spreading each layer across many GPUs—and sometimes many servers—but it creates a new one: how do you coordinate those shards, route requests, and share KV cache fast enough to feel like one accelerator? This orchestration gap is exactly what NVIDIA Dynamo is built to close.  ### Introducing NVIDIA Dynamo NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities such as:  - **Disaggregated prefill & decode inference** – Maximizes GPU throughput and facilitates trade off between throughput and latency. - **Dynamic GPU scheduling** – Optimizes performance based on fluctuating demand - **LLM-aware request routing** – Eliminates unnecessary KV cache re-computation - **Accelerated data transfer** – Reduces inference response time using NIXL. - **KV cache offloading** – Leverages multiple memory hierarchies for higher system throughput Built in Rust for performance and in Python for extensibility, Dynamo is fully open-source and driven by a transparent, OSS (Open Source Software) first development approach. ### Installation The following examples require a few system level packages. Recommended to use Ubuntu 24.04 with a x86_64 CPU. See [docs/support_matrix.md](docs/support_matrix.md) ``` apt-get update DEBIAN_FRONTEND=noninteractive apt-get install -yq python3-dev python3-pip python3-venv libucx0 python3 -m venv venv source venv/bin/activate pip install "ai-dynamo[all]" ``` > [!NOTE] > To ensure compatibility, please refer to the examples in the release branch or tag that matches the version you installed. ### Building the Dynamo Base Image Although not needed for local development, deploying your Dynamo pipelines to Kubernetes will require you to build and push a Dynamo base image to your container registry. You can use any container registry of your choice, such as: - Docker Hub (docker.io) - NVIDIA NGC Container Registry (nvcr.io) - Any private registry Here's how to build it: ```bash ./container/build.sh docker tag dynamo:latest-vllm <your-registry>/dynamo-base:latest-vllm docker login <your-registry> docker push <your-registry>/dynamo-base:latest-vllm ``` Notes about builds for specific frameworks: - For specific details on the `--framework vllm` build, see [here](examples/llm/README.md). - For specific details on the `--framework tensorrtllm` build, see [here](examples/tensorrt_llm/README.md). Note about AWS environments: - If deploying Dynamo in AWS, make sure to build the container with EFA support using the `--make-efa` flag. After building, you can use this image by setting the `DYNAMO_IMAGE` environment variable to point to your built image: ```bash export DYNAMO_IMAGE=<your-registry>/dynamo-base:latest-vllm ``` > [!NOTE] > We are working on leaner base images that can be built using the targets in the top-level Earthfile. ### Running and Interacting with an LLM Locally To run a model and interact with it locally you can call `dynamo run` with a hugging face model. `dynamo run` supports several backends including: `mistralrs`, `sglang`, `vllm`, and `tensorrtllm`. #### Example Command ``` dynamo run out=vllm deepseek-ai/DeepSeek-R1-Distill-Llama-8B ``` ``` ? User › Hello, how are you? ✔ User · Hello, how are you? Okay, so I'm trying to figure out how to respond to the user's greeting. They said, "Hello, how are you?" and then followed it with "Hello! I'm just a program, but thanks for asking." Hmm, I need to come up with a suitable reply. ... ``` ### LLM Serving Dynamo provides a simple way to spin up a local set of inference components including: - **OpenAI Compatible Frontend** – High performance OpenAI compatible http api server written in Rust. - **Basic and Kv Aware Router** – Route and load balance traffic to a set of workers. - **Workers** – Set of pre-configured LLM serving engines. To run a minimal configuration you can use a pre-configured example. #### Start Dynamo Distributed Runtime Services First start the Dynamo Distributed Runtime services: ```bash docker compose -f deploy/metrics/docker-compose.yml up -d ``` #### Start Dynamo LLM Serving Components Next serve a minimal configuration with an http server, basic round-robin router, and a single worker. ```bash cd examples/llm dynamo serve graphs.agg:Frontend -f configs/agg.yaml ``` #### Send a Request ```bash curl localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "messages": [ { "role": "user", "content": "Hello, how are you?" } ], "stream":false, "max_tokens": 300 }' | jq ``` ### Local Development If you use vscode or cursor, we have a .devcontainer folder built on [Microsofts Extension](https://code.visualstudio.com/docs/devcontainers/containers). For instructions see the [ReadMe](.devcontainer/README.md) for more details. Otherwise, to develop locally, we recommend working inside of the container ```bash ./container/build.sh ./container/run.sh -it --mount-workspace cargo build --release mkdir -p /workspace/deploy/sdk/src/dynamo/sdk/cli/bin cp /workspace/target/release/http /workspace/deploy/sdk/src/dynamo/sdk/cli/bin cp /workspace/target/release/llmctl /workspace/deploy/sdk/src/dynamo/sdk/cli/bin cp /workspace/target/release/dynamo-run /workspace/deploy/sdk/src/dynamo/sdk/cli/bin uv pip install -e . export PYTHONPATH=$PYTHONPATH:/workspace/deploy/sdk/src:/workspace/components/planner/src ``` #### Conda Environment Alternately, you can use a conda environment ```bash conda activate <ENV_NAME> pip install nixl # Or install https://github.com/ai-dynamo/nixl from source cargo build --release # To install ai-dynamo-runtime from source cd lib/bindings/python pip install . cd ../../../ pip install ".[all]" # To test docker compose -f deploy/metrics/docker-compose.yml up -d cd examples/llm dynamo serve graphs.agg:Frontend -f configs/agg.yaml ``` ", Assign "at most 3 tags" to the expected json: {"id":"13943","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"