CUDA alternatives, AMD's unified stack, on-premise cost shifts and GPU supply constraints are challenging Nvidia's traditional hardware and software lock-in. Infrastructure decisions are becoming more contested across consumer and data center segments.
The first Loom SDK v0.1 from AMD marks the start of the Hyperloom roadmap announced at Advancing AI, aiming to optimize agentic workloads across CPUs, GPUs and NPUs. For local deployment, the signal is twofold: AMD wants to unify the ROCm ecosystem, ...
An IT vendor has listed a used AI supercomputer on eBay for $60 million: 9,600 Nvidia A100 GPUs, 4,200 AMD EPYC CPUs and 300 decommissioned racks, with free shipping. The listing highlights how AI hardware life cycles are feeding a secondary market r...
Demand for local AI infrastructure is growing as cloud costs weigh on budgets. Supply for Asus RTX Spark is tightening, signaling a market shifting resources toward on-premises computing. The trend highlights pressure on hardware, lead times, and dep...
Competition with CUDA is widening: AWS and Google are pushing alternative software stacks, while AMD and Intel aim to lower the cost of AI cluster switches. Reduced dependence on NVIDIA's software and cheaper networking shift the TCO equation for lar...
According to reports, Nvidia is no longer accepting RTX 5090 orders from board partners and has also blocked backorders. If confirmed, the move signals demand exceeding supply and reignites the hardware question for on-premise inference: availability...
A modification to Nvidia RTX 20, 30 and 40 series cards removes a limit that halves performance in AI workloads. The case touches the core of local inference and TCO for those choosing consumer hardware.
A figure attributed to Jensen Huang reignites the debate over NVIDIA's business model: $2,000 per GPU per year as software revenue. For on-premise cluster operators, cost shifts from CapEx to recurring OpEx, affecting TCO, operational autonomy, and s...
