Enterprise deployments have shown a consistent pattern: when organizations move from static GPU allocation to dynamic scheduling, cluster usage becomes far more dynamic. Over-quota resources (the shared pool beyond guaranteed quotas) become one of the most heavily utilized resource types. Teams regularly exceed their guaranteed allocations, resulting in higher GPU utilization and more compute time for researchers. This makes over-quota fairness critical. When a significant portion of cluster value comes from this shared pool, that pool needs to be divided fairly over time. The classical statel
Ensuring Balanced GPU Allocation in Kubernetes Clusters with Time-Based Fairshare | NVIDIA Technical Blog… The scheduler won’t allocate beyond fair share while the Vision team still has pending jobs claiming their portion. The LLM team must wait until Vision team’s over-quota usage drops. LLM team’s post-training job waits…and waits…and waits. …
Data Center / Cloud Accelerating Vision AI Pipelines with Batch Mode VC-6 and NVIDIA Nsight Apr 02, 2026 By Andreas Kieslinger , Ricardo Monteiro , Guendalina Cobianchi , Adam Kelly and Nima Shirvanian Discuss 0 Discuss 0 L T F R E AI-Generated Summary Like Dislike Architectural changes in the VC-6… …
… Analyze and measure hardware memory usage In addition to CPU memory, GPU and multimedia allocations can impact available memory. $ sudo cat /sys/kernel/debug/nvmap/iovmm/clients This shows memory usage across processes using NvMap e.g., CUDA, video pipelines . …
Data Center / Cloud Building Token‑Metered AI Services on Telco AI Factories May 21, 2026 By Waleed Badr and Amogh Dendukuri Discuss 0 Discuss 0 L T F R E AI-Generated Summary Like Dislike Telcos are building sovereign AI factories based on the NVIDIA Cloud Partner reference architecture to provide… …
Networking / Communications Optimizing Communication for Mixture-of-Experts Training with Hybrid Expert Parallel Feb 02, 2026 By Fan Yu , Tong Liu and Kai Sun Discuss 0 Discuss 0 L T F R E AI-Generated Summary Like Dislike Hybrid-EP, an efficient Expert Parallel EP communication library, leverages … …
… It also explains the usage flow of this library, highlights available performance tools, and provides a few examples to help you get started. What is NIXL? NIXL is an open source library for accelerating point-to-point data transfers in AI inference frameworks. …
… Pytorch Trace improvements - Added forward methods and training parameters. Python Sampling improvements - Better backtrace display in timeline tooltips and events view. VRAM Usage Recipe Analyze Windows graphics resource allocation, migration, event history, allocation callstack and perf markers. …
Agentic AI / Generative AI How to Build Deep Agents for Enterprise Search with NVIDIA AI-Q and LangChain Mar 18, 2026 By Sean Lopp , Sam Pastoriza , Ajay Thorve , Chantal D Gama Rose and Victor Moreira Discuss 1 Discuss 1 L T F R E AI-Generated Summary Like Dislike The NVIDIA AI-Q blueprint, built … …
… In this example, we leverage the fact that LangChain’s FAISS from documents method conveniently generates the embeddings for the document chunks and also stores them in the FAISS vector store in one function call. from langchain community.vectorstores import FAISS from langchain nvidia ai endpoints… …
… 11 MIN READ Feb 18, 2026 Unlock Massive Token Throughput with GPU Fractioning in NVIDIA Run:ai As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. NVIDIA Run:ai addresses these challenges... …