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Cohere announced a public beta of Confidential Computing for its managed inference product Model Vault, running on NVIDIA H100 GPUs and Microsoft Azure confidential computing infrastructure. The feature adds hardware-backed isolation, GPU memory encryption, remote attestation, and an end-to-end encrypted request path so customers can verify the runtime and protect data in use during inference.
An NVIDIA Developer article outlines how NVLink 6 is designed to provide multi-layer resiliency for large-scale AI training clusters, aiming to help operators maximize continuous GPU output and maintain productivity in massive AI "factories."
NVIDIA's Personal AI Router (PAIR), now in beta, enables combining the inference capacity of multiple computers on a local network and automatically distributing AI requests among them. It is aimed at local multi-agent AI workloads where multiple independent model calls can otherwise overwhelm a single GPU.
NVIDIA Developer published guidance on sizing GPUs for AI inference workloads, focusing on balancing performance and total cost of ownership to avoid overspending. The article aims to help organizations understand trade-offs when planning inference infrastructure and costs.
NVIDIA has released CUDA Python 1.0, presenting stable APIs and a unified foundation intended to give Python developers fuller access to CUDA and GPU capabilities without requiring C++ extension toolchains. The release aims to simplify building GPU-accelerated Python applications across the NVIDIA platform.
An NVIDIA Developer article argues that modern AI factories are increasingly power-constrained and that the key metric has shifted from GPU count to AI output per watt. It presents NVIDIA DSX MaxLPS as an approach for improving performance-per-watt in AI deployments and discusses related system- and infrastructure-level considerations.
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