NVIDIA's CUDA kernel fusion technique optimizes memory usage and minimizes kernel launch overhead, achieving speedups of up to 3x for workloads like Mixture-of-Experts models and large language model training. Kernel fusion addresses high memory bandwidth consumption by keeping intermediate data in registers or shared memory, reducing global memory traffic. Benchmarks showed a naive implementation of the operation sum(abs(x)) required two kernels and 3GB of memory traffic, completing in 3.51 milliseconds, while a manually fused kernel reduced traffic to 1GB and completed in 1.18 milliseconds—a 3x improvement. Effective memory bandwidth approached 90% of the theoretical peak of an RTX 4090. NVIDIA’s CUDA Toolkit 13.3, released in May 2026, includes new abstractions like CUDA Tile programming. NVIDIA’s MLPerf Training 6.0 results cited kernel fusion as a key driver behind a 1.3x throughput improvement on Blackwell GPUs. Developers can use manual fusion, compiler fusion via tools like PyTorch’s torch.compile, or explicit APIs like NVIDIA’s cuda.compute API, each with trade-offs in performance, ease of use, and predictability.