22 results for “HBM”
NVIDIA launched NVLink Fusion with NVHBM on August 26, 2026, giving hyperscalers a standardized path to deploy custom XPUs alongside NVIDIA GPUs. The co-designed memory and interconnect stack promises 30% higher bandwidth, 25% more compute area, and 15% lower HBM power versus standard HBM4e.
Industry reports say Micron is pushing High Bandwidth Memory capacity toward roughly 100,000 wafers a month by end-2026 — about double last year’s estimated run rate — while lifting 12-high HBM4 share for NVIDIA’s Vera Rubin. Micron has not issued a matching September IR confirmation of the wafer target.
AMD, Super Micro Computer and Spectro Cloud have jointly unveiled AMD Instinct Coder, a turnkey enterprise AI coding platform designed to give. Enterprise AI coding has moved from individual developer experimentation to a platform-level.
Advanced Micro Devices (AMD) shares rose sharply on Monday after the company announced a major expansion of its strategic partnership with Microsoft (MSFT). This announcement is a critical validation for AMD's AI strategy.
NVIDIA validated Alibaba's Qwen3.8-Flash-Next 176B MoE model on its GB300 NVL72 rack, achieving over 16K tokens per second per GPU. The hybrid GDN/QSA architecture keeps memory and compute bounded for million-token agentic coding workloads.
NVIDIA unveiled NVLink Fusion, letting hyperscalers plug custom XPUs into its 72‑XPU NVLink domain and MGX rack architecture. The program cuts non‑differentiated engineering so silicon teams can ship custom accelerators faster while reusing proven power, cooling, and cluster software.
NVIDIA declared Scale-In the fifth pillar of AI networking infrastructure, powered by BlueField-4 DPUs, DOCA software, and Spectrum-X Ethernet. The architecture offloads security, storage, and telemetry from host CPUs at up to 800 Gb/s to keep pace with agentic AI factories.
NVIDIA detailed how Spectrum-X Ethernet uses hardware-accelerated adaptive routing, congestion control, and plane load balancing to solve traditional Ethernet's failures at giga-scale AI training. The platform delivers 1.6x performance over off-the-shelf Ethernet, near-perfect multi-tenant isolation, and 2.68 ms failover versus 1.08 seconds for standard Ethernet.
NVIDIA took a minority stake in data-center site developer Cloverleaf Infrastructure to accelerate AI factory construction across the U.S. The partnership integrates NVIDIA's DSX platform early in site design to maximize compute output per megawatt.
NVIDIA launched DSX MaxLPS, a full-stack platform that dynamically reallocates stranded power across GPU racks to fit up to 40% more compute in the same facility envelope. The system combines real-time power steering, workload-aware profiles, and 45°C liquid cooling to boost performance per watt by 1.3–1.5x on Blackwell and Vera Rubin systems.
NVIDIA published a practical observability framework mapping telemetry tools to every layer of DGX and HGX AI factory infrastructure. The guide helps teams catch gray failures like InfiniBand bit-error drift before they waste multi-hour training runs.
NVIDIA published a tutorial showing how KAI Scheduler and vCluster let three teams share one L40S GPU while each gets an isolated Kubernetes control plane. The pattern scales to thousands of nodes and gives platform teams a way to consolidate GPU hardware without sacrificing tenant autonomy.