At the rack level, DSX MaxLPS offers further flexibility through workload-specific power profiles. Much like the quiet, balanced, and high-performance power modes that client PC users are familiar with, Nvidia has produc…
As AI shifts from chatbots to autonomous agents, open models are serving market demands for full control over where AI runs and how it’s deployed and evolves.
Nvidia’s slide does a good job illustrating, but it’s worth noting the difference compared to traditional SMT nonetheless. With traditional SMT, resources are time-sliced between threads, leading to gaps between BP and d…
The unencrypted module let Ukrainian investigators read the drone’s terrain maps and targeting code.
Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just …
According to OpenRouter data , agentic AI workloads consume 15x more tokens than a simple chat request. Why?
According to OpenRouter data , agentic AI workloads consume 15x more tokens than a simple chat request. Why?
An FPGA bridges the synchronous LPU domain and the asynchronous world of host I/O and GPU hand-offs, and Nvidia’s Dynamo runtime, together with an LPU extension to CUDA, orchestrates the split. The company put the gains …
To make this possible, w orld models learn how physical environments behave, what may happen next and which following actions make sense. They can generate physically grounded world and action data, simulate future state…
Agents spend much of their runtime off the GPU. A model that writes code, for example, calls tools, queries databases, and parses results by leaning on the host CPU between every inference pass; idle GPUs waiting on that…
