Originally published at: Smart Multi-Node Scheduling for Fast and Efficient LLM Inference with NVIDIA Run:ai and NVIDIA Dynamo | NVIDIA Technical Blog
The exponential growth in large language model complexity has created challenges, such as models too large for single GPUs, workloads that demand high throughput and low latency, and infrastructure that must coordinate thousands of interconnected components seamlessly. The NVIDIA Run:ai v2.23 release addresses these challenges through an integration with NVIDIA Dynamo—a high-throughput, low-latency inference framework…
If we deploy blueprint with helm, would the run:ai memory swap still work? How?