As the artificial intelligence technology progressed (AI), efficient and scalable inference solutions have increased rapidly. Soon it is expected that AI inference will become more important than training, because companies focus on fast -leading models to introduce real -time forecasts. This transformation emphasizes the need for solid infrastructure to support large amounts of data with minimal delays.
Inference is necessary in industries such as autonomous vehicles, fraud detection and medical diagnostics in real time. However, he has unique challenges, significantly when scaling, to meet the requirements of such tasks, such as video streaming, live data analysis and customer observations. Traditional AI models try to effectively deal with these high tasks, often leading to high costs and delays. As companies expand their artificial intelligence capabilities, they need solutions to manage large amounts of applications regarding application without devoting efficiency or increasing costs.
It is there Nvidia Dynamo Enters. Launched in March 2025, Dynamo is a new AI framework designed to solve the problems of inference AI on a large scale. It helps companies accelerate the charges of application while maintaining high results and reducing costs. Built on solid NVIDIA GPU architecture and integrated with tools such as miracles, tensort and triton, Dynamo changes the managing method of AI's application, making it easier and more efficient for companies of all size.
The growing challenge of inference AI on a large scale
AI inference is the process of using a previously trained machine learning model to predict data from real data, and is necessary for many AI applications in real time. However, traditional systems often encounter difficulties in coping with the growing demand for AI inference, especially in areas such as autonomous vehicles, fraud detection and healthcare diagnostics.
The demand for artificial intelligence in real time increases rapidly, powered by the need for quick, at the place of decision making. May 2024 Forrester The report stated that 67% of companies integrates generative artificial intelligence with their activities, emphasizing the importance of artificial intelligence in real time. Inference is at the basis of many AI tasks, such as enabling self -propelled cars to make quick decisions, detect fraud in financial transactions and help in medical diagnoses such as analysis of medical images.
Despite this demand, traditional systems try to cope with the scale of these tasks. One of the main problems is not using the GPU. For example, the use of GPU in many systems remains about 10% to 15%, which means that significant computing power is unused. As the loading of artificial intelligence increases, additional challenges, such as memory restrictions and cache, which cause delays and reduce overall performance.
Achieving a low delay is of key importance for real -time AI applications, but many traditional systems try to keep up, especially when using the cloud infrastructure. AND McKinsey report Reveals that 70% of AI projects do not meet its goals due to the quality of data and integration problems. These challenges emphasize the need for more efficient and scalable solutions; At this point, Nvidia Dynamo enters.
Optimization of AI inference using NVIDIA Dynamo
NVIDIA Dynamo is a modular structure of Open Source, which optimizes large tasks regarding AI inference in dispersed multi-gpu environments. It aims to solve common challenges in generative models of artificial intelligence and reasoning, such as not using GPU, bottlenecks and inefficient routing of demands. Dynamo combines the optimization of hardware innovation to solve these problems, offering a more efficient solution for AI at a high level.
One of the key features of Dynamo is the disagregated serving architecture. This approach separates the intensive computational phase, which supports contextual processing from the decoding phase, which includes the production of token. By assigning each phase to separate GPU clusters, Dynamo allows for independent optimization. The Prefill phase uses high GPU memory for faster context consumption, while the decoded phase uses GPU optimized with delay to efficient tokens. This separation improves bandwidth, thanks to which models such as Lama 70b twice as fast.
It covers the planner of GPU resources, who dynamically plans GPU allocation based on real -time use, optimizing the loads between pre -sales and decoded clusters to prevent excessive design and idle cycles. Another key function is an intelligent router with KV cache, which ensures that incoming demands are focused to the GPU containing the relevant data of cache key memory (KV), thus minimizing redundant calculations and improving performance. This function is particularly beneficial for multi -stage reasoning models that generate more tokens than standard models of large languages.
. Nvidia Wnference Tranxfer Library (NIXL) It is another key component that allows communication with a low delay between the GPU and heterogeneous memory/memory levels, such as HBM and NVME. This feature supports the download of subordinate KV cache, which is crucial for time sensitive tasks. The distributed KV cache manager also helps to relieve access to cache data to system memory or SSD less often, releasing GPU memory for active calculations. This approach increases the general system performance by up to 30x, especially for large models, such as Deepseek-R1 671B.
NVIDIA Dynamo integrates with a full pile of NVIDIA, including miracles, tensorrt and Blackwell GPU, while supporting popular application, such as VLLM and Tensorrt-LLM. The benchmarks show up to 30 times higher GPU tokens per second for models such as Deepseek-R1 in GB200 NVL72 systems.
As the successor of the Triton application server, Dynamo is designed for AI factories requiring scalable, profitable solutions. Benefits of autonomous systems, real -time analysis and multi -model agency work. Its open source and modular design also allows easy adjustment, thanks to which it is adapted to various AI loads.
Real applications and the impact of the industry
Nvidia Dynamo showed value in various industries in which AI inference in real time is crucial. It improves autonomous systems, real -time analytics and AI factory, enabling highly apology AI applications.
Companies like Together AI I used the dynamo to scale the application loads, reaching up to 30x the capacity strengthening when starting the Deepseek-R1 models on the NVIDIA Blackwell GPU. In addition, intelligent Routing Dynamo and GPU planning improve the performance in large -scale AI implementation.
Competitive advantage: Dynamo vs. alternatives
NVIDIA Dynamo offers key advantages compared to alternatives such as AWS Intuentuntia and Google TPU. It has been designed for effective support for high burden on artificial intelligence, optimization of GPU planning, memory management and routing demands to improve the efficiency of many GPUs. Unlike AWS Inxturel, which is closely related to the AWS cloud infrastructure, Dynamo provides flexibility by servicing the hybrid cloud and local implementation, helping companies avoid blocking suppliers.
One of the strong pages of Dynamo is the modular architecture of the Open Source type, enabling companies to adapt the frame based on their needs. Optimizes each stage of the application process, ensuring that AI models work smoothly and efficiently, at the same time to best use the available computing resources. Focusing on scalability and flexibility, Dynamo is suitable for enterprises looking for a profitable and high -performance solution to the AI application.
Lower line
NVIDIA Dynamo transforms the world of AI inference, providing a scalable and efficient solution to the challenges that companies with AI applications are facing in real time. His Open Source and the modular design allows him to optimize the use of GPU, better manage memory and directly direct demands, making it ideal for large -scale AI tasks. By separating key processes and enabling dynamic adaptation of the GPU, Dynamo increases efficiency and reduces costs.
Unlike traditional systems or competitors, Dynamo supports a hybrid cloud and local configurations, providing companies with greater flexibility and reducing dependence on each supplier. Thanks to the impressive performance and adaptability, NVIDIA Dynamo establishes a new AI application standard, offering companies advanced, profitable and scalable solution for their needs AI.
