From Geekbench 7's AI benchmarks to AMD's ROCm updates and Nvidia's GPU price hikes, the hardware landscape is rapidly evolving to support local AI inference. Google, Meta, and AMD are investing heavily in custom silicon and developer tools.
Nvidia is reportedly preparing to increase prices for its GeForce RTX GPUs by up to 30%. This move has significant implications for on-premise AI deployment strategies, raising the Total Cost of Ownership and prompting companies to reconsider local h...
A fresh DIGITIMES report captures a wave of infrastructure investment led by Google and Meta, driven by the relentless growth of Large Language Models. Between custom silicon and increasingly locked-in clouds, AI-RADAR dissects what this means for th...
At Advancing AI, AMD announced a strict six-week release cycle for the ROCm platform, offering predictability and stability for developers and system administrators. For on-prem deployments, it means planned updates without surprises and reduced risk...
AMD unveiled ROCm.ai, a platform tailored for agentic AI, claiming up to 3.3x inference speedups. The move bolsters the company’s software push in a landscape that increasingly values on‑premise deployments, where hardware efficiency and ecosystem ma...
Meta and Google team up to bring PyTorch's Helion DSL to cloud TPUs. The compiler generates optimized Pallas code, picking between two pipelining strategies according to sequence length. It hits 838 TFLOPs on a flash attention workload on TPU v7. GPU...
Geekbench 7 brings AI benchmarks, realistic media workloads, and CUDA support. No longer just synthetic numbers, but metrics that matter for those running LLMs locally—a sign that benchmarking is evolving for the on-premise inference era.
With the GitHub update just before Lisa Su's keynote, AMD strengthens on-device model support for Ryzen AI. Optimizations and new models drive data sovereignty and enterprise adoption.
