Gpu

Theoretically, it looks simple: define a goal, collect appropriate data, train the model with this data, validate it, and then monitor and measure the model’s quality, making adjustments if necessary. In real life, however, you deal with separate and major problems at every step. Shall we begin? :) Is your defined goal clear enough? What will the model be used for? Is there a previously built model for this? Is this a research and development job, or is it solving a previously unsolved problem? Or is the main goal just keeping up with the AI trend? If it’s just for the trend, definitely don’t develop a model. Because it’s not a goal. There’s not even anything worth doing the project for. ...

The GPU world is experiencing a leak frenzy as details about NVIDIA’s next-generation GeForce RTX 50-series graphics cards continue to emerge. Built on the revolutionary Blackwell architecture, the RTX 5090 and RTX 5080 promise to push the boundaries of gaming and AI performance in 2025. From massive power requirements to groundbreaking ray tracing capabilities, these upcoming cards are generating serious buzz among enthusiasts and professionals alike. Blackwell Architecture: The Foundation for Next-Gen Performance NVIDIA’s Blackwell architecture represents a fundamental shift in GPU design, emphasizing neural rendering techniques to overcome traditional performance limitations. According to various leaks, the architecture doubles ray tracing capabilities compared to previous generations, potentially delivering up to 4x performance gains in ray-traced scenarios. ...