# Install all core components pip install cuda-toolkit[all]
Enhanced Developer Productivity, Next-Gen Hardware Support, and Streamlined HPC Workflows. cuda toolkit 126
This represents a shift from the previous practice where multiple CUDA versions could coexist more easily. Therefore, careful planning is advised before upgrading production environments. # Install all core components pip install cuda-toolkit[all]
Use Nsight Compute for deep-dive kernel profiling. It analyzes hardware counter metrics to tell you exactly why a specific kernel is slow—whether it is bound by memory bandwidth, compute limitations, or poor instruction pipelines. Next-Gen Hardware Support
One of the most significant performance-focused developments in CUDA 12.x has been the optimization of CUDA Graphs. NVIDIA's improvements between version 11.8 and 12.6 resulted in dramatic reductions in CPU overhead for graph-based workloads. The table below summarizes these key performance gains:
