At Google Cloud Next, Google announced its next-generation AI accelerators: the TPU v8t “Sunfish” for training and the TPU v8i “Zebrafish” for inference, along with its new Virgo data center fabric. From Google’s blog posts, it is clear these chips are optimized for “the agentic era”: training frontier mixture-of-experts models at the hundreds-of-thousands-of-chips scale, and
At Google CloudNext, IBM and Google Cloud announced an expanded collaboration to address a recurring challenge for enterprise customers: modernizing core systems and operationalizing AI across hybrid and multi-cloud environments without increasing complexity. The joint effort focuses on improving interoperability across platforms, data, and tooling while maintaining operational consistency and security. The partnership combines Google
NVIDIA and Google Cloud used Google Cloud Next in Las Vegas to outline a new phase of their long-standing engineering partnership, introducing updates to the Google Cloud AI Hypercomputer platform to scale agentic and physical AI for production environments. The companies continue to co-design infrastructure spanning silicon, systems, networking, and software to support increasingly complex
When we published our story on Oregon State University’s plankton imaging research last November, the headline was the science: AI-accelerated infrastructure aboard research vessels, processing terabytes of ocean data in near real-time before the ship ever reached port. But something else happened quietly in the weeks that followed. Word spread across campus about what a
At SUSECON in Prague, SUSE introduced SUSE AI Factory with NVIDIA, a pre-validated enterprise AI software stack that simplifies deployment and operations from local development through production. Built on SUSE AI and NVIDIA AI Enterprise, the platform is designed for organizations seeking to build, govern, and scale AI workloads across edge locations, core data centers,
IBM introduced a content-aware storage (CAS) architecture that integrates AI data processing directly into the storage layer. The approach targets retrieval-augmented generation (RAG) workflows by embedding document vectorization within the storage system, reducing the need for external preprocessing pipelines. CAS shifts a core RAG function, document embedding using large language model-based techniques, into storage infrastructure.
NetApp announced an expanded collaboration with Google Cloud, formalized through a four-year enterprise agreement to accelerate the deployment of NetApp storage within Google Distributed Cloud (GDC) Air-Gapped environments. Delivered with World Wide Technology (WWT), the offering targets sovereign cloud use cases that require strict data residency, security, and operational isolation. The joint solution integrates NetApp’s
Supermicro’s JumpStart program has established itself as one of the more useful tools in the pre-purchase evaluation toolkit for AI infrastructure. Rather than a scripted demo in a shared environment, JumpStart gives qualified users free, time-boxed, bare-metal access to real production servers via SSH, IPMI, and VNC, enabling them to run workloads on actual hardware.
AMD has released its MLPerf Inference v6.0 results, positioning the Instinct MI355X GPU as a scalable inference platform across single-node, multinode, and heterogeneous deployments. The submission extends beyond incremental gains by adding new workloads, demonstrating cluster-scale throughput exceeding 1 million tokens per second, and validating reproducibility across a growing partner ecosystem. CDNA 4 Architecture Targets
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
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