NVIDIA has published results for MLPerf Inference v6.0, highlighting system-level gains driven by tight co-design across hardware, software, and models. The company positions inference throughput and token economics as the primary metrics for AI factory performance, moving beyond peak accelerator specifications to measured output under real workloads. In this round, systems built on NVIDIA Blackwell
WEKA announced integration of its NeuralMesh platform with the NVIDIA STX reference architecture, positioning its Augmented Memory Grid as a core component for next-generation AI infrastructure. The combined solution targets one of the primary constraints in large-scale inference environments: memory limitations that impact performance, cost, and scalability. Running on NeuralMesh, WEKA’s Augmented Memory Grid extends
WEKA has announced the general availability of its NeuralMesh AI Data Platform, an enterprise-focused, composable infrastructure designed for AI factory deployments. Built on the NVIDIA AI Data Platform reference architecture, NeuralMesh AIDP provides an integrated stack that delivers AI-ready data to production environments, with a focus on accelerating time-to-deployment for large-scale AI applications. The platform
At GTC 2026, IBM and NVIDIA announced a significant expansion of their more than decade-long partnership, focusing on moving AI from pilot phases to full-scale production. The collaboration targets several critical bottlenecks in enterprise AI adoption, including GPU-native data analytics, intelligent document processing, and infrastructure for regulated environments. The joint effort aims to provide a
During GTC 2026, VDURA showcased updates to its Data Platform that improve GPU utilization and storage efficiency in AI environments. The announcement includes the general availability of Remote Direct Memory Access (RDMA), a preview of its Context-Aware Tiering technology, and validated infrastructure setups based on AMD EPYC Turin CPUs and NVIDIA ConnectX-7 networking. The updates
The LPU, or Language Processing Unit, is a custom AI inference accelerator designed and built by Groq, Inc. Founded in 2016 by Jonathan Ross, a former Google engineer credited as one of the original inventors of the TPU, Groq spent years developing a deterministic, software-defined processor architecture from the ground up. Unlike GPUs, which rely
HPE has announced a significant expansion of the NVIDIA AI Computing by HPE portfolio, introducing integrated systems designed to scale enterprise AI deployments while maintaining security and governance. The update focuses on co-engineered, validated architectures intended to accelerate time-to-value for AI inferencing and model development. HPE CEO Antonio Neri and NVIDIA CEO Jensen Huang positioned
At NVIDIA GTC 2026, Intel announced that its Intel Xeon 6 processors are being used as the host CPUs for NVIDIA DGX Rubin NVL8 systems. This design win extends the established use of Xeon within NVIDIA’s GPU platforms and underscores the processor’s role in orchestrating large-scale, GPU-accelerated AI infrastructure. As AI workloads transition toward massive,
HPE has unveiled updates to the NVIDIA AI Computing by HPE portfolio to support large-scale AI factories and next-generation supercomputers. The offerings combine compute, GPUs, networking, liquid cooling, software, and services into full‑stack solutions designed for at‑scale and sovereign environments. NVIDIA AI Integrated into HPE Exascale Supercomputing Platform. Argonne National Laboratory, HLRS, Hudson River Trading,
At NVIDIA GTC, Lenovo introduced an expanded phase of its Lenovo Hybrid AI Advantage with NVIDIA, positioning the portfolio as an end-to-end path for production AI inferencing across client devices, enterprise infrastructure, and large-scale AI cloud deployments. The announcement centers on accelerating AI adoption, cutting time-to-first-token (TTFT), and improving per-token economics as organizations move from
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