NVIDIA continues to dominate the AI hardware market, evidenced by high demand for its GPUs and strategic moves like potential software licensing. However, competition is rising from cloud providers (AWS, Google) and chipmakers (AMD, Intel) offering CUDA alternatives and cost-effective solutions, while hardware reliability issues also emerge.
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...
A repair shop received eight GeForce RTX 5090 and RTX 4090 boards with melted 16-pin connectors in two days. The failure pattern points to a power delivery weakness in high-end consumer GPUs, with direct implications for self-hosted inference and fin...
