NVIDIA Dynamo：分布式AI推理的高吞吐量框架

BLUF: Executive Summary

NVIDIA Dynamo is an open-source, high-throughput, low-latency inference framework specifically designed for serving generative AI and reasoning models in multi-node distributed environments. According to industry reports, Dynamo addresses the critical orchestration gap created by tensor-parallelismA distributed computing technique that spreads individual neural network layers across multiple GPUs or servers to handle models that exceed single-device memory and compute capabilities. approaches, enabling efficient coordination across multiple GPUs and servers while maintaining inference engine agnosticism.

Understanding the AI Inference FrameworkSoftware platform that runs trained machine learning models in production environments, optimized for speed, efficiency, and scalability when applying learned models to new inputs. Landscape

What is an AI Inference FrameworkSoftware platform that runs trained machine learning models in production environments, optimized for speed, efficiency, and scalability when applying learned models to new inputs.?

An AI inference frameworkSoftware platform that runs trained machine learning models in production environments, optimized for speed, efficiency, and scalability when applying learned models to new inputs. is a software system that optimizes and manages the deployment of trained machine learning models for real-time predictions. These frameworks handle critical tasks including model loading, request routing, resource allocation, and performance optimization to serve production workloads efficiently.

The Multi-GPU, Multi-Node Challenge

Large language models have rapidly outgrown the memory and compute capabilities of individual GPUs. While tensor-parallelismA distributed computing technique that spreads individual neural network layers across multiple GPUs or servers to handle models that exceed single-device memory and compute capabilities. distributes model layers across multiple accelerators, it introduces significant orchestration complexities:

• Coordinating shards across distributed systems
• Efficient request routing
• Fast KV cacheKey-Value cache that stores intermediate computations during transformer model inference, significantly reducing redundant computations for repeated tokens and improving inference efficiency. sharing
• Maintaining low-latency performance

NVIDIA Dynamo directly addresses these challenges through its specialized architecture.

NVIDIA Dynamo: Technical Architecture and Capabilities

Core Design Principles

Built in Rust for performance and Python for extensibility, Dynamo follows an open-source first development approach. The framework is designed to be inference engine agnostic, supporting multiple backend engines including TensorRT-LLM, vLLM, and SGLang.

Key Technical Features

Disaggregated ServingAn architectural approach that separates prefill (initial prompt processing) and decode (token generation) operations across different compute resources to optimize throughput and latency trade-offs. Architecture

Dynamo implements a disaggregated prefill and decode inference approach that maximizes GPU throughput while facilitating optimal trade-offs between throughput and latency.

Dynamic Resource Management

• Dynamic GPU Scheduling: Optimizes performance based on fluctuating demand patterns
• LLM-Aware Request Routing: Eliminates unnecessary KV cacheKey-Value cache that stores intermediate computations during transformer model inference, significantly reducing redundant computations for repeated tokens and improving inference efficiency. recomputation through intelligent routing
• KV-Aware Routing: Advanced routing based on KV cacheKey-Value cache that stores intermediate computations during transformer model inference, significantly reducing redundant computations for repeated tokens and improving inference efficiency. availability and location

Performance Optimization Technologies

• Accelerated Data Transfer: Reduces inference response time using NVIDIA's NIXL technology
• KV CacheKey-Value cache that stores intermediate computations during transformer model inference, significantly reducing redundant computations for repeated tokens and improving inference efficiency. Offloading: Leverages multiple memory hierarchies for higher system throughput
• SLA-Based Planning: Intelligent deployment optimization to meet service level agreements

Framework Support and Compatibility

Comprehensive Feature Matrix

Dynamo supports a wide range of advanced features across multiple inference engines:

Feature	vLLM	SGLang	TensorRT-LLM
Disaggregated ServingAn architectural approach that separates prefill (initial prompt processing) and decode (token generation) operations across different compute resources to optimize throughput and latency trade-offs.	✅	✅	✅
KV-Aware Routing	✅	✅	✅
SLA-Based Planner	✅	✅	✅
KVBM	✅	🚧	✅
Multimodal Support	✅	✅	✅
Tool Calling	✅	✅	✅

Note: Full feature matrix includes additional capabilities such as LoRA support, request migration, and speculative decoding.

Deployment and Implementation

Installation Requirements

System Requirements:

• Ubuntu 24.04 with x86_64 CPU
• Python development headers: sudo apt install python3-dev
• Recommended package manager: uv

Quick Start Implementation

# 1. Install uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Create virtual environment
uv venv venv
source venv/bin/activate
uv pip install pip

# 3. Install Dynamo with preferred engine
uv pip install "ai-dynamo[sglang]"  # Replace with [vllm] or [trtllm]

# 4. Run sanity check
./deploy/sanity_check.py

Service Deployment Configuration

Dynamo supports multiple deployment scenarios with flexible service discovery options:

Deployment	etcd	NATS	Notes
Kubernetes	❌ Not required	❌ Not required	K8s-native discovery
Local Development	❌ Not required	❌ Not required	Use --store-kv file
KV-Aware Routing	—	✅ Required	NATS for KV event messaging

Performance and Benchmarking

Industry Validation

Recent performance benchmarks demonstrate Dynamo's capabilities:

• Moonshot AI's Kimi K2: Achieved 10x inference speedup on GB200 hardware
• Mistral AI: Runs Mistral Large 3 with 10x faster inference
• Baseten: Achieved 2x faster inference performance
• Dell PowerScale Integration: 19x faster Time to First Token (TTFT) with NIXL

Benchmarking Tools

Dynamo provides comprehensive benchmarking capabilities:

• Benchmarking Guide: Compare deployment topologies using AIPerf
• SLA-Driven Deployments: Optimize configurations to meet specific service level agreements

Integration with Inference Engines

vLLM Integration

uv pip install ai-dynamo[vllm]
python -m dynamo.vllm --help

Note: vLLM attempts to allocate full context length KV cacheKey-Value cache that stores intermediate computations during transformer model inference, significantly reducing redundant computations for repeated tokens and improving inference efficiency. at startup. Adjust with --context-length parameter for memory-constrained environments.

SGLang Integration

apt install -y libnuma-dev
uv pip install ai-dynamo[sglang]
python -m dynamo.sglang --help

TensorRT-LLM Integration

Recommended deployment uses NGC PyTorch containers with version matching between TensorRT-LLM and PyTorch container images.

API and Interface Specifications

OpenAI-Compatible Frontend

Dynamo provides a high-performance OpenAI-compatible HTTP API server written in Rust, supporting:

• Standard OpenAI API endpoints
• Prompt templating and tokenization
• Streaming and non-streaming responses
• OpenAPI 3 specification at /openapi.json

Request Example

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

Production Deployment Considerations

Kubernetes Deployment

Follow the Quickstart Guide for Kubernetes deployments, leveraging native K8s service discovery and TCP request plane.

Distributed Deployments

For non-Kubernetes distributed deployments:

• Run etcd directly: ./etcd
• Configure NATS with JetStream: nats-server -js
• Use Docker Compose for quick setup: docker compose -f deploy/docker-compose.yml up -d

Future Development and Community

Dynamo maintains transparent development through:

• Open-source first approach
• Regular office hours and community engagement
• Active design proposals and roadmap discussions
• Comprehensive documentation and recipe collections

Conclusion

NVIDIA Dynamo represents a significant advancement in distributed AI inference frameworkSoftware platform that runs trained machine learning models in production environments, optimized for speed, efficiency, and scalability when applying learned models to new inputs.s, specifically addressing the orchestration challenges of multi-GPU, multi-node deployments. Its engine-agnostic design, comprehensive feature set, and proven performance improvements make it a compelling solution for organizations scaling generative AI and reasoning model deployments in production environments.

For detailed implementation guides, feature matrices, and community resources, refer to the official Dynamo documentation and GitHub repository.