AMD today unveiled ROCm 7.0, a massive update to its open GPU software platform for AI workloads across datacenter racks and even client devices. The release focuses on support for the new Instinct MI350 accelerator series, built on CDNA 4. It pairs hardware-level refinements, including advanced HBM utilization and expanded datatype handling, with software improvements designed to enhance throughput for both training and inference. AMD classifies this release as a generational step that widens deployment choices for enterprises, from single-node servers to multi-rack clusters. Company benchmarks shared with partners indicate multi-fold gains over previous ROCm 6.0 versions on select large-model tasks, and AMD shows tighter integration with open-source serving stacks to help teams transition from prototype to production more quickly.

Under the hood, ROCm 7.0 delivers algorithmic and systems advances intended to reduce cost and improve utilization: enhanced attention and reasoning primitives, sparse mixture-of-experts techniques, and support for FP4, FP6, and FP8 precision formats that trade a bit of accuracy for massive memory savings. ROCm 7.0 also focuses on scale and manageability by enabling GPU-direct communications, distributed inference across nodes with vLLM-d and DeepEP compatibility, and cluster tools such as AMD Resource Manager and a new AI Workbench for orchestration and monitoring. To smooth adoption, AMD provides prebuilt Docker images with quantized model examples and turnkey stacks for common frameworks. AMD is also expanding endpoint support to Ryzen AI processors and Radeon RX series GPUs, so developers can deploy consistent pipelines from edge devices to cloud racks. ROCm 7 is available now through AMD's developer resources, with documentation, examples, and enterprise support to help teams get started.

Symantec, a division of Broadcom Inc., is partnering with Google Cloud to embed generative AI (gen AI) into the Symantec Security platform in a phased rollout that will give customers a significant technical edge for detecting, understanding, and remediating sophisticated cyber attacks.

Symantec is leveraging the Google Cloud Security AI Workbench and security-specific large language model (LLM)--Sec-PaLM 2-across its portfolio to enable natural language interfaces and generate more comprehensive and easy-to-understand threat analyses. With Security AI Workbench-powered summarization of complex incidents and alignment to MITRE ATT&CK context, security operations center (SOC) analysts of all levels can better understand threats and be able to respond faster. That, in turn, translates into greater security and higher SOC productivity.

NVIDIA today announced NVIDIA AI Workbench, a unified, easy-to-use toolkit that allows developers to quickly create, test and customize pretrained generative AI models on a PC or workstation - then scale them to virtually any data center, public cloud or NVIDIA DGX Cloud. AI Workbench removes the complexity of getting started with an enterprise AI project. Accessed through a simplified interface running on a local system, it allows developers to customize models from popular repositories like Hugging Face, GitHub and NVIDIA NGC using custom data. The models can then be shared easily across multiple platforms.

"Enterprises around the world are racing to find the right infrastructure and build generative AI models and applications," said Manuvir Das, vice president of enterprise computing at NVIDIA. "NVIDIA AI Workbench provides a simplified path for cross-organizational teams to create the AI-based applications that are increasingly becoming essential in modern business."