NVIDIA Releases Dynamo Deployment Recipes for Leading Korean Open AI Models

Image: NVIDIA · Source

NVIDIA unveils Dynamo-optimized recipes to efficiently deploy top Korean open AI models across multiple GPUs, enhancing performance and accelerating production use.

NVIDIA has introduced deployment recipes optimized for its Dynamo framework that support several prominent open AI models developed in South Korea. These include A.X K2 by SK Telecom, K-EXAONE 2.0 from LG AI Research, Solar Open 2 by Upstage, and Motif-3 by Motif Technologies. As AI models increase in complexity and size beyond the capacity of a single GPU, they require distribution across multiple GPUs with careful management of request routing and inter-GPU communication to maintain high performance.

NVIDIA Dynamo is an open source distributed inference-serving framework designed to deploy such large models efficiently in multi-node data center environments. It coordinates the splitting and routing of inference requests across GPUs to maximize throughput and minimize latency. The newly released deployment recipes are tailored for NVIDIA B200 GPUs and provide developers with ready-made guidance to avoid trial-and-error configurations, yielding up to double the performance immediately upon deployment.

This initiative aims to ease the path for developers and organizations in Korea to move their open AI models into production environments quicker and with greater reliability. By leveraging these pre-optimized recipes, AI services built on these models can become more accessible to a wider audience, aligning with NVIDIA’s vision of democratizing AI through efficient, scalable deployment solutions.

For developers working with large-scale AI models, NVIDIA’s Dynamo recipes represent a valuable resource to manage the complexity of multi-GPU inference routing while maximizing hardware utilization. This release underscores the growing collaboration between NVIDIA and Korean AI research entities to advance AI capabilities globally through open ecosystems and optimized infrastructure.