Tag: inference

This tutorial explores how NVIDIA Run:ai v2.23 and NVIDIA Dynamo synergize to overcome the complexities of multi-node LLM inference, focusing on gang scheduling and topology-aware placement for enhanced speed and efficiency.

The recent DeepSeek Day, marked by the release of the DeepSeek-R1 model, has ignited industry discussions about the future of AI infrastructure. While some foresee a slowdown in the AI build-out due to a new, potentially lower-cost model, a deeper analysis suggests this development signals a crucial evolution towards more accessible and efficient AI applications, rather than an end to the current trajectory.

This analysis delves into the critical economic factors influencing AI inference performance across NVIDIA and AMD GPUs. It examines how different hardware architectures, software optimizations, and market dynamics impact the cost-effectiveness and overall value proposition of GPU-based inference solutions, particularly for large language models and complex AI workloads.