You don’t “run a model” - You run Kernels The model is just a graph The Inference Engine is scheduler / optimizer / executor But the actual work? That happens in the Kernels - MatMul Kernels - Attention Kernels - RMSNorm Kernels - KV cache Kernels - Quantized linear Kernels - Sampling Kernels - Fused “please don’t write this back to memory 9 times” Kernels Same model, same GPU, same VRAM Wildly different performance Because one stack is using optimized fused Kernels that understand your hardware And the other stack is playing hot potato with tensors through 47 tiny launches and pretending the GPU is the problem Bad Kernels make people say: “this model is slow” Good Kernels make people say: “wait how is this running locally?” This is why Inference Engines and the Kernels implemented within them matter The model is the recipe The hardware is the kitchen The Kernels are the knives, pans, burners, and the chef not cutting onions with a spoon Most people benchmark models The real ones benchmark the Kernels underneath