NVIDIA AI OPEN SOURCES Dynamo: An open source inference library to accelerate and climb AI reasoning models in AI factories

The rapid advance of artificial intelligence (ai) has led to the development of complex models capable of understanding and generating text as a human. The implementation of these large language models (LLM) in real world applications presents significant challenges, particularly in the optimization of performance and management of computational resources efficiently.

<h3 class="wp-block-heading" id="h-challenges-in-scaling-ai-reasoning-models”>Challenges in the IA reasoning models scale

As the ai ​​models grow in complexity, their implementation demands increase, especially during the inference phase, the stage where models generate results based on new data. The key challenges include:

• Resource assignment: Balancing computational loads in extensive GPU groups to avoid bottlenecks and underutilization is complex.
• Latency reduction: Ensuring rapid response times is essential for user satisfaction, which requires low latency inference processes.
• Cost management: The substantial computational requirements of the LLMs can lead to the growing operating costs, which makes profitable solutions essential.

Presentation of Nvidia Dynamo

In response to these challenges, Nvidia has introduced DynamoAn open source inference library designed to accelerate and climb IA reasoning models efficiently and profitably. As the successor of the Nvidia Triton Inference Server , Dynamo offers an adapted modular framework for distributed environments, allowing a perfect scale of inference workloads in large GPU fleets. ​

Innovations and technical benefits

Dynamo incorporates several key innovations that collectively improve inference performance:

• Breaking service: This approach separates the context phases (prefesta) and generation (decoding) of the inference of LLM, assigning them to different GPUs. By allowing each phase to optimize independently, the broken service improves the use of resources and increases the number of inference applications met by GPU. ​
• GPU resource planner: The dynamically dynamically adjusts the GPU allocation in response to the fluctuating demand of users, avoiding the supervision or submission and guarantee of optimal performance. ​
• Smart router: This component efficiently directs the incoming inference requests in large Flees of GPU, minimizing expensive reputations taking advantage of the knowledge of the previous applications, known as KV Cache. ​
• Low latency communication library (Nixl): Nixl accelerates data transfer between GPU and through various types of memory and storage, reducing inference response times and simplifying the complexities of data exchange.
• KV cache manager: By downloading lower frequency inference data than those that are less frequently accessed to more profitable memory and storage devices, Dynamo reduces general inference costs without affecting user experience. ​

Performance insights

Dynamo's impact on inference performance is substantial. By serving the Deepseek-R1 671b open source reasoning model at NVIDIA GB200 NVL72, Dynamo increased performance, measured in tokens per second per GPU, for up to 30 times. In addition, serving the model calls 70b in Nvidia Hopper resulted in more than a double increase in performance. ​

These improvements allow IA services providers to satisfy more inference requests by GPU, accelerate response times and reduce operating costs, thus maximizing the yields of their accelerated calculation investments. ​

Conclusion

Nvidia Dynamo represents a significant advance in the deployment of ai reasoning models, addressing critical challenges on the scale, efficiency and profitability. Its open source nature and compatibility with the main inference backends of ai, including Pytorch, SGLANG, NVIDIA Tensorrt -LLM and VLLM, Empower companies, new companies and researchers to optimize the ai ​​model that serves in breakdown of inference. By taking advantage of Dynamo's innovative characteristics, organizations can improve their ai capacities, providing faster and efficient services to meet the growing demands of modern applications.

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