Latest insights, features, and updates about CUDA development, GPU optimization, and AI-powered coding
By Jaber JaberYou're switching between four different applications to profile a single kernel. Nsight Compute for metrics. Visual Studio for code. A terminal for compilation. nvidia-smi in another window. By the time you find the memory bottleneck, you've forgotten what you were optimizing.
By Jaber JaberGetting CUDA working shouldn't take days. After analyzing hundreds of installation failures, I've compiled every fix you need for PATH errors, driver mismatches, WSL2 problems, and more. Stop fighting with nvcc and start coding.
By Jaber JaberWhen researchers plot model cross-entropy loss against compute on a log-log scale, the result is a near-straight line: loss falls predictably as compute, model size, or training tokens increase. That empirical regularity - the scaling law - lets teams forecast returns, but it also shows the limit: buying more GPUs gives diminishing marginal returns.
