AI Scale Inference: Exploring the High Performance Architecture of NVIDIA Dynamo
With the development of artificial intelligence (AI) technology, the need for effective and scalable inference solutions has grown rapidly. Soon, as companies focus on running models quickly to make real-time predictions, AI inference will become more important than training. This transformation emphasizes that a strong infrastructure requires minimal latency to process large amounts of data.
Inference is crucial in industries such as autonomous driving, fraud detection and real-time medical diagnosis. However, it has unique challenges when scaling to meet the needs of tasks such as video streaming, real-time data analytics, and customer insights. Traditional AI models are difficult to handle these high-throughput tasks effectively, which often lead to high costs and delays. As businesses expand AI capabilities, they need solutions to manage large numbers of inference requests without sacrificing performance or increasing costs.
This is where NVIDIA Dynamo enters. Launched in March 2025, Dynamo is a new AI framework designed to address the challenges of large-scale AI inference. It can help businesses accelerate their inference workload while maintaining strong performance and reducing costs. Built on NVIDIA’s powerful GPU architecture and integrated with tools like CUDA, Tensorrt, and Triton, Dynamo is changing the way companies manage AI inferences, making it easier and more efficient for businesses of all sizes.
The increasingly stringent challenge of large-scale inference
AI inference is the process of predicting from real-world data using pre-trained machine learning models and is crucial for many real-time AI applications. However, traditional systems often face difficulties in dealing with growing demand for AI inference, especially in areas such as autonomous vehicles, fraud detection and healthcare diagnosis.
The demand for real-time AI is growing rapidly due to the need for rapid, on-site decision-making. The May 2024 Forrester report found that 67% of enterprises incorporate generative AI into their operations, highlighting the importance of real-time AI. Inference is at the heart of many AI-driven tasks, such as enabling autonomous vehicles to make quick decisions, detect fraud in financial transactions, and assisting in medical diagnosis, such as analyzing medical images.
Despite this need, traditional systems are working hard to handle the scale of these tasks. One of the main problems is the inadequate utilization of GPUs. For example, GPU utilization in many systems is still around 10% to 15%, which means a significant lack of computing power. As the workload of AI inference increases, other challenges arise, such as memory limiting and caching thrash, which can lead to latency and reduce overall performance.
Achieving low latency is critical for real-time AI applications, but many traditional systems are difficult to keep up, especially when using cloud infrastructure. A McKinsey report shows that 70% of AI projects fail to achieve their goals due to data quality and integration issues. These challenges highlight the need for more efficient and scalable solutions. This is where NVIDIA Dynamo stepped in.
Optimize AI inference with NVIDIA Dynamo
NVIDIA Dynamo is an open source modular framework that optimizes large-scale AI inference tasks in distributed multi-GPU environments. Its purpose is to address the common challenges of generating AI and inference models such as GPU underutilization, memory bottlenecks, and inefficient request routing. Dynamo combines hardware-aware optimization with software innovation to solve these problems, providing more effective solutions for high-demand AI applications.
One of the key features of Dynamo is its classification of service architecture. This approach separates the computationally intensive pre-filling phase from the processing of the decoding phase involving token generation, which processes context processing. Dynamo allows independent optimization by assigning each stage to a different GPU cluster. The pre-filling phase uses a high-memory GPU for faster context ingestion, while the decoding phase uses a delay-optimized GPU for efficient token streams. This separation improves throughput, making models such as the Llama 70B very fast.
It includes a GPU resource planner that dynamically schedules GPU allocations based on real-time utilization, optimizing workloads between pre-fill and decode clusters to prevent over-provisioning and idle cycles. Another key feature is the KV cache smart router, which ensures incoming requests hold relevant key-value (KV) cache data directly for the GPU, minimizing redundant computing and increasing efficiency. This feature is particularly beneficial for multi-step inference models that produce more tokens than standard large language models.
NVIDIA infers that the Tranxfer library (NIXL) is another key component that allows low-latency communication between GPU and heterogeneous memory/storage layer between HBM and NVME (e.g. HBM and NVME). This feature supports submillisecond kV cache retrieval, which is critical for time-sensitive tasks. The distributed KV cache manager also helps less access to cache data to system memory or SSD, freeing up GPU memory for active computing. This approach can improve overall system performance by 30 times, especially for large models such as the DeepSeek-R1 671B.
NVIDIA Dynamo integrates with NVIDIA’s full stack, including Cuda, Tensorrt and Blackwell GPUs, while supporting popular inference backends such as Vllm and Tensorrt-Llm. For models like DeepSeek-R1 on GB200 NVL72 systems, the benchmark test marks up to 30 times per second.
As the successor to Triton inference servers, Dynamo is designed for AI factories that require scalable, cost-effective inference solutions. It benefits autonomous systems, real-time analysis and multi-model agent workflows. Its open source and modular design can also be easily customized to adapt it to a variety of AI workloads.
Real-world application and industry impact
NVIDIA Dynamo shows value in industries where real-time AI inference is crucial. It enhances autonomous systems, real-time analytics and AI factories, enabling high-throughput AI applications.
Companies like AI use Dynamo to scale inference workloads, which improves 30x capability when running the DeepSeek-R1 model on NVIDIA Blackwell GPUs. In addition, Dynamo’s smart request routing and GPU planning improves the efficiency of large-scale AI deployments.
Competitive Advantages: Generators and Alternatives
Nvidia Dynamo has key advantages over alternatives such as AWS reasoning and Google TPU. It is designed to effectively handle large-scale AI workloads, optimize GPU scheduling, memory management, and request routing to improve performance on multiple GPUs. Unlike AWS reasoning closely related to AWS cloud infrastructure, Dynamo provides flexibility by enabling hybrid cloud and on-premises deployments, helping businesses avoid vendor lock-in.
One of the advantages of Dynamo is its open source modular architecture, allowing companies to customize frameworks based on their needs. It optimizes every step of the inference process, ensuring that the AI ​​model runs smoothly and efficiently while making full use of available computing resources. Dynamo focuses on scalability and flexibility for businesses looking for cost-effective and high-performance AI reasoning solutions.
Bottom line
NVIDIA Dynamo changes the world of AI inference by providing scalable and effective solutions for the challenges enterprises face through real-time AI applications. Its open source and modular design enables it to optimize GPU usage, better manage memory and route requests more efficiently, making it ideal for large-scale AI tasks. By separating critical processes and allowing the GPU to adjust dynamically, the generator can improve performance and reduce costs.
Unlike traditional systems or competitors, Dynamo supports hybrid cloud and on-premises setups, allowing businesses to be more flexible and less reliant on any provider. With impressive performance and adaptability, NVIDIA Dynamo sets new standards for AI inference, providing companies with advanced, cost-effective and scalable solutions to meet their AI needs.
