Dynamo is a new open source framework from Nvidia that addresses the complex challenges of scaling AI inference operations. Introduced at the GPU Technology Conference, this framework optimizes how large language models run across multiple GPUs, balancing individual performance with system-wide throughput. CEO Jensen Huang described it as “the operating system of an AI factory,” drawing parallels to how the original dynamo sparked the industrial revolution for energy production.
Why Scaling AI Models is Harder Than You Think
AI models are getting enormous and are used in workflows where multiple models need to talk to each other. When deploying these massive models:
- They’re too big to fit on a single GPU
- You need to spread them across multiple GPUs or servers
- Getting all these parts to work together efficiently is complicated
Under the Hood of NVIDIA Dynamo
Think of NVIDIA Dynamo as an air traffic control system for AI processing. It includes four key components:
vLLM and similar libraries like TensorRT-LLM and SGLang represent the current generation of inference serving frameworks designed to optimize the deployment of large language models (LLMs). These tools provide efficient mechanisms for handling token generation, memory management, and batch processing to improve throughput and reduce latency when serving AI models. NVIDIA Dynamo complements these frameworks by functioning as a higher-level distributed inference system that can utilize them as backends while adding crucial capabilities for large-scale deployments. Unlike traditional serving approaches, Dynamo introduces disaggregated serving that separates prefill and decode phases across different GPUs, dynamic GPU scheduling based on workload fluctuations, intelligent request routing to minimize KV cache recomputation, and accelerated data transfer between GPUs. This layered architecture allows developers to leverage their existing vLLM knowledge while gaining Dynamo’s distributed scaling capabilities across potentially thousands of GPUs.
How Dynamo Tackles the Reasoning Model Challenge
Reasoning AI models present unique challenges for inference systems due to their substantially increased token requirements and computational demands—typically requiring 20 times more tokens and 150 times more compute than standard LLMs. NVIDIA Dynamo is specifically architected to address these challenges through its components.
- The Smart Router intelligently distributes workloads and tracks KV cache locations across large GPU fleets, significantly reducing costly recomputations when handling multi-step reasoning chains.
- The Distributed KV Cache Manager allows offloading less-frequently accessed cache to more economical storage tiers, enabling cost-effective management of the massive context windows needed for complex reasoning.
- Additionally, Dynamo’s GPU Planner dynamically rebalances resources between prefill and decode phases to accommodate the asymmetric computational patterns characteristic of reasoning tasks, where initial context processing may be extraordinarily compute-intensive while subsequent reasoning steps have different resource profiles.
These capabilities make Dynamo well-suited for the next generation of reasoning-focused AI applications.
Where Dynamo Stands Today
Currently available on GitHub as open-source software, Dynamo builds upon NVIDIA’s experience with Triton Inference Server (which has over a million downloads and established production use), but takes a more specialized approach for LLMs. While NVIDIA claims Dynamo can boost inference throughput by up to 30x when running DeepSeek-R1 models on Blackwell hardware through innovations like disaggregated prefill/decode stages and dynamic GPU scheduling, these performance metrics remain largely unverified by independent parties. The framework incorporates cutting-edge features like LLM-aware request routing, cross-GPU data transfer optimizations, and KV cache offloading across memory hierarchies, but it remains relatively new and unproven in large-scale production environments. As some developers have noted, potential adopters should proceed carefully, given historical difficulties implementing NVIDIA’s previous inference products—even with direct access to their development team.
For enterprises seeking production-ready implementation, NVIDIA plans to include Dynamo with their NIM microservices as part of NVIDIA AI Enterprise, suggesting a transition path from their more established Triton inference platform toward this newer, LLM-optimized solution.
Ray Serve and vLLM: Flexible Inference for Complex Workloads
While NVIDIA Dynamo offers specialized performance for LLM inference, teams seeking more flexibility may want to consider Ray Serve. Built on the Ray distributed computing framework, Ray Serve provides a versatile, framework-agnostic solution for deploying models across various ML frameworks alongside custom Python business logic. Notably, Ray Serve can integrate seamlessly with vLLM and SGLang, allowing users to leverage the same LLM optimization techniques while benefiting from Ray’s broader ecosystem.
Ray Serve particularly shines in scenarios requiring complex model composition, diverse model types beyond just LLMs, or integration with existing Ray-based workflows. Its autoscaling capabilities and flexible resource allocation (including fractional GPU support) make it well-suited for heterogeneous environments or teams balancing multiple AI workloads. For organizations that value adaptability and a Python-centric development experience, the combination of Ray Serve with vLLM offers a compelling alternative to specialized frameworks like Dynamo.