As artificial intelligence (AI) technology advances, the need for efficient and scalable inference solutions is rapidly increasing. Soon, AI inference is expected to be more important than training as it focuses on companies quickly implementing fast models to make real-time predictions. This transformation highlights the need for a robust infrastructure to process large amounts of data with minimal latency.
Inference is essential in industries such as autonomous vehicles, fraud detection, and real-time medical diagnosis. However, when scaling to meet the demands of tasks such as video streaming, live data analytics, customer insights, it has its own challenges. Traditional AI models struggle to efficiently handle these high-throughput tasks, often leading to high costs and delays. As businesses expand their AI capabilities, solutions need to manage large numbers of inference requests without sacrificing performance or increasing costs.
This is where Nvidia Dynamo appears. Released in March 2025, Dynamo is a new AI framework designed to tackle the challenges of AI reasoning at scale. This helps businesses accelerate their inference workloads while maintaining strong performance and reducing costs. Built on Nvidia’s robust GPU architecture and integrated with tools such as Cuda, Tensort, Triton, and more, Dynamo changes the way companies manage AI inference, making it easier and more efficient for businesses of all sizes.
The Growing Challenges of Large-scale AI Inference
AI inference is the process of making predictions from real data using pre-trained machine learning models and is essential for many real-time AI applications. However, traditional systems often face difficulties in dealing with the increasing demand for AI inference, especially in areas such as self-driving cars, fraud detection, and healthcare diagnostics.
The demand for real-time AI is rapidly increasing due to the need for high-speed, on-site decision-making. A May 2024 Forrester report found that 67% of companies integrated generated AI into their businesses, highlighting the importance of real-time AI. Inference is at the heart of many AI-driven tasks, such as enabling self-driving cars to make quick decisions, detecting financial transaction fraud, and assisting with medical diagnosis such as analyzing medical images.
Despite this demand, traditional systems have struggled to handle the scale of these tasks. One of the main issues is the full use of the GPU. For example, GPU usage on many systems remains around 10%-15%, meaning that critical computing power is underutilized. As AI inference workloads increase, additional challenges arise, such as memory limits and cache thrashing, resulting in delayed overall performance reduction.
While achieving low latency is important for real-time AI applications, many traditional systems struggle to keep up, especially when using cloud infrastructure. A report from McKinsey revealed that 70% of AI projects fail to meet their goals due to data quality and integration issues. These challenges underscore the need for a more efficient and scalable solution. This is where Nvidia Dynamo intervene.
Optimize AI inference with Nvidia Dynamo
Nvidia Dynamo is an open source modular framework that optimizes large-scale AI inference tasks in a distributed multi-GPU environment. It aims to tackle the general challenges of generated AI and inference models, including low GPU utilization, memory bottlenecks, and inefficient request routing. Dynamo combines hardware-enabled optimization with software innovation to address these issues, providing a more efficient solution for high-demand AI applications.
One of the key features of the dynamo is its disassembled serving architecture. This approach separates the computationally intensive prefill phase that handles context processing from the decoding phase that involves token generation. By assigning each phase to a different GPU cluster, Dynamo allows for independent optimization. The Prefill phase uses high memory GPUs for fast context intake, while the decoding phase uses efficient token streaming with latency-optimized GPUs. This separation improves throughput and doubles faster models like the Llama 70B.
This includes a GPU resource planner that dynamically schedules GPU allocations based on real-time utilization, optimizing workloads between pre-clusters and decoded clusters to prevent excessive deployment and idle cycles. Another important feature is the KV Cache-Aware Smart Router. This directs incoming requests to the GPU that holds the associated key value (KV) cache data, minimizing redundant calculations and improving efficiency. This feature is particularly useful for multi-step inference models that generate more tokens than standard large language models.
The NVIDIA Inference Tranxfer Library (NIXL) is another important component that allows low-latency communication between GPUs such as HBM and NVME and heterogeneous memory/storage layers. This feature supports sub-millisecond KV cache search, which is important for time-sensitive tasks. A distributed KV cache manager also helps offload less frequently accessed cache data to system memory or SSDs, freeing up GPU memory for active calculations. This approach enhances overall system performance by up to 30 times, especially on larger models such as the DeepSeek-R1 671b.
Nvidia Dynamo integrates with Nvidia’s full stack, including Cuda, Tensort and Blackwell GPUs, while supporting popular inference backends such as Vllm and Tensort-llm. The benchmarks show you up to 30x tokens per second on models such as the DeepSeek-R1 on GB200 NVL72 systems.
As a successor to Triton Inference Server, Dynamo is designed for AI factories that require scalable, cost-effective inference solutions. Useful for autonomous systems, real-time analytics, and multi-model agent workflows. Additionally, open source and modular design allow for easy customization and adapt to a wide range of AI workloads.
Real-world applications and industry impact
Nvidia Dynamo shows value across industries where real-time AI inference is key. Powers autonomous systems, real-time analytics, and AI factories to enable high-throughput AI applications.
Companies like AI have used Dynamo to scale their inference workloads and achieved up to 30x capacity increases when running DeepSeek-R1 models on Nvidia Blackwell GPUs. Additionally, Dynamo’s intelligent request routing and GPU scheduling improves the efficiency of large-scale AI deployments.
Competitiveness: Dynamos and alternatives
Nvidia Dynamo offers more important advantages than alternatives such as AWS Imentia and Google TPU. It is designed to efficiently handle large AI workloads and request GPU scheduling, memory management and routing to improve performance on multiple GPUs. Unlike AWS Imwerantia, which is closely tied to AWS cloud infrastructure, Dynamo offers flexibility by supporting both hybrid cloud and on-premises deployments, helping businesses avoid vendor lock-in.
One of the strengths of Dynamo is its open source modular architecture, allowing businesses to customize their frameworks based on their needs. Optimize every step in the inference process to ensure that your AI model runs smoothly and efficiently, while making the most of the available computational resources. With a focus on scalability and flexibility, Dynamo is suitable for businesses looking for cost-effective, high-performance AI inference solutions.
Conclusion
Nvidia Dynamo is transforming the world of AI inference by providing scalable and efficient solutions to the challenges facing businesses with real-time AI applications. Open source and modular design allow you to optimize GPU usage, manage memory better, and request route requests more effectively, making it ideal for large AI tasks. By isolating key processes and allowing GPUs to dynamically adjust, Dynamo increases performance and reduces costs.
Unlike traditional systems and competitors, Dynamo supports hybrid cloud and on-premises setups, providing greater flexibility for your business and reducing dependency on providers. With impressive performance and adaptability, Nvidia Dynamo sets new standards for AI inference and offers businesses a sophisticated, cost-effective, scalable solution for their AI needs.
