Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...
For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster...
Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the...
Introduction to intelligent conveyor control The TeSys™ Island load management and energy monitoring system represents a cutting-edge solution for modern conveyor operations, addressing critical needs such as real-time monitoring, fault differentiation, and operator safety. By leveraging embedded functionalities, this technology minimizes wiring and simplifies system... The post Optimizing conveyor operations through smart technology solutions appeared first on Schneider Electric Blog .
Digital sovereignty is top of mind for many business leaders, but exactly what that entails will look different for each leader and each organization. For…
Finance organizations are rapidly deploying AI workloads to support fraud detection, risk modeling, customer service, and other high-performance applications. As GPU-based AI infrastructure expands, many IT teams are discovering that traditional power architectures were not designed to handle the rapid load fluctuations these workloads create.... The post Why finance organizations need AI-tolerant UPSs for resilient AI infrastructure appeared first on Schneider Electric Blog .
How Speculators and Mooncake enabled multi-node DSpark training for Kimi K3.
Novita AI has open-sourced Chord, a high-performance W4A16 MoE CUDA kernel for Kimi K2.x serving shapes, with a Humming-compatible indexed path and grouped SM90
Google is exploring a new data center project in Lea County, New Mexico. While discussions are ongoing, we recognize residents are asking questions about data center dev…
vime and RL-Kernel align selected-token logprobs bit for bit across Megatron training and vLLM rollout on AMD Instinct MI300X, with zero mismatches across 200 G
[watermarking] Dual-key gumbel-max watermarking for speculative decod…
Kimi K3 serving optimizations across scheduling, KDA prefix caching, ReplaySSM state recovery, PD disaggregation and state offload, parallelism, MoE, and GPU ke
Every new cloud, AI provider or partner that an enterprise chooses to work with could mean another connection to design, provision and manage. As…
Until now, circuit-breakers have relied on the electrical arc generated by the separation ofmetallic contacts to interrupt both normal and fault currents. This principle, introducedover a century ago, has remained the foundation of conventional circuit-breakertechnology. Transistors such as MOSFETs* or IGBTs** have been widely used... The post 1 new IEC standard for semiconductor circuit-breakers appeared first on Schneider Electric Blog .
Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second.
vllm-proto 0.1.0
v0.29.0 Highlights This release features 594 commits from 277 contributors (91 new)! Model Runner V2 is now the default for all models ( #53183 ), completing the rollout that began with pooling models ( #48290 ). MRV2 also gained CUDA graph memory profiling for KV cache auto-sizing ( #53306 ), batch-sharded sampling that cuts per-step logits memory by 1/TP ( #50465 ), prompt embeds ( #42963 ), extract_hidden_states speculation ( #49811 ), padded FULL cudagraph dispatch for uniform decode under spec decode ( #53407 ), and DP-sync skipping before EAGLE/MTP draft prefill ( #53694 ). MRV1 remains in use for a few ROCm models and features MRV2 doe
In Kubernetes, resource allocation has historically been a static decision made during a Pod's initial scheduling and placement. With the graduation of the core in-Place Pod resize feature to General Availability in v1.35, application developers and cluster operators gained the powerful ability to dynamically adjust CPU and memory allocations of running containers without incurring disruptive restarts or application downtime. However, in-place resizing introduced a unique resource scheduling gap: if a running Pod requested a resource scale-up that exceeded the host node's allocatable headroom, the Kubelet was forced to mark the request as Def
Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA...
Every enterprise AI strategy eventually runs into the same question: not just which models to use, but where to run them. That question is…
The post Energy in hands appeared first on Schneider Electric Blog .
NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two...
Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid.
Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software...
A host-centric framework for scaling KV cache across host memory, filesystems, object stores, and remote peers — reducing recomputation and increasing serving c
Animation showing Google’s approach to responsible energy growth in Finland
In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains, and...
The rise of distributed AI workloads is changing the way networks function. Enterprises need connections to more endpoints in more places, and they need…
Data center GPUs physically last 5+ years, but economic replacement cycles are 2-4 years due to rapid performance gains. Resale or GPU-as-a-Service models help maximize value.