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Guardian investigation finds apparent discrepancy between what tech company has said about its AI capacity – and the number of advanced chips it has in operationThe chips are quite small and some can be held in the palm of a hand. They are fundamental to the development of artificial intelligence models – and the world’s […]
Are Microsoft’s AI plans being held back by a shortage of chips? Read More »
TL;DR An NVIDIA Spectrum-X fabric should not be approached as a conventional Ethernet refresh with faster switches. Distributed AI creates synchronized, high-bandwidth traffic patterns in which congestion, packet loss, path imbalance, and tail latency can slow an entire training job. A production design should: Separate the high-performance compute fabric from storage, in-band services, and out-of-band
How to Build an NVIDIA Spectrum-X Ethernet Fabric for an AI Factory Read More »
Red Hat has launched asago, an open-source community project that aims to turn AI governance policy into production-ready deployment code. The project describes itself as an automated, auditable workflow that connects the “fragmented steps, tools, and requirements” of engineering and compliance teams. With regulation such as the EU AI Act now taking effect, Red Hat
Red Hat, NVIDIA, IBM back project turning AI policy into code Read More »
TL;DR NVIDIA Dynamo is preferable to a standalone inference server when the serving problem extends beyond one process or one GPU node. It introduces a Kubernetes-native control plane for distributed inference graphs, separate prefill and decode workers, KV-cache-aware routing, model loading, topology-aware placement, autoscaling, fault recovery, Gateway API integration, and multi-node execution. This tutorial uses
How to Deploy NVIDIA Dynamo on Kubernetes for Distributed LLM Inference Read More »
Investors scramble to make sense of a shock Chinese challenge to the dominance of western chipmakersEven for the rollercoaster world of AI, last week was particularly volatile as investors scrambled to keep up with developments that threatened the dominance of the largest western chipmakers.It began with a double whammy. On Monday, the Chinese memory chipmaker
Stock market turmoil sheds stark light on the opaque AI economy Read More »
Introduction Enterprise inference standardization often begins with a benchmark chart. That is usually where the first mistake occurs. A team runs one model on one GPU configuration, increases concurrency until the system saturates, records the highest tokens-per-second result, and declares a winner. The result may be technically repeatable while still being operationally irrelevant. It says
Open-source questions stir frank discussion – and both sides have clear economic incentives for where they landHello, and welcome to TechScape. This week we’ll be looking at a debate over the future of artificial intelligence that’s dividing the tech industry, as well as how the European Union gave Google a slap on the wrist for
Debate over AI’s future divides Silicon Valley as China gains ground Read More »
Samsung and SK Hynix fall by more than 10% amid renewed fears over AI spending and Chinese competitionBusiness live – latest updatesThe sell-off in AI stocks has intensified, driving South Korea’s stock market down to its lowest level in three months.Investors continued to ditch chip stocks on Tuesday, amid rising concerns about the huge amount
AI sell-off intensifies as investors ditch chip stocks Read More »
TL;DR NVIDIA Run:ai can turn a shared Kubernetes GPU cluster into a governed multi-tenant platform by organizing workloads into departments and projects, assigning guaranteed GPU quotas per node pool, and allowing controlled over-quota use when capacity would otherwise remain idle. The design depends on four controls working together: Quota establishes the resource entitlement for a
How to Configure Multi-Tenant GPU Scheduling with NVIDIA Run:ai Read More »
TL;DR NVIDIA GPU monitoring needs more than a utilization chart. A production design should collect device telemetry with DCGM Exporter, scrape it with Prometheus, visualize fleet and workload behavior in Grafana, and alert on conditions that require action. The runbook must also preserve per-pod context, control metric cardinality, distinguish low utilization from genuine performance problems,
How to Monitor NVIDIA GPUs with DCGM Exporter, Prometheus, and Grafana Read More »
