Running GenAI models is easy. Scaling them to thousands of users, not so much. This guide details avenues for scaling AI workloads from proofs of concept to production-ready deployments, covering API integration, on-prem deployment considerations, hardware requirements, and tools like vLLM and Nvidia NIMs.
K8S-native cluster-wide deployment for vLLM. Provides a reference implementation for building an inference stack on top of vLLM, enabling scaling, monitoring, request routing, and KV cache offloading with easy cloud deployment.
Scaling Reinforcement Learning (RL) to surpass O1 in deep learning models
This article discusses the importance of API scalability for handling traffic spikes, improving user experience, and optimizing resource utilization. It covers key concepts, scaling strategies with code examples, and best practices for scaling the API layer.
Recent volumetric brain reconstructions reveal high anatomic complexity. Research shows brain anatomy satisfies universal scaling laws, implying criticality in the cellular brain structure. Findings enable comparisons of structural properties across different organisms.
