You Don't Run a Model, You Run Kernels: Why Inference Performance Hides in Fused Kernels
EAccelerate_42 · x · 2026-09-03
TheAhmadOsman argues that inference performance lives in kernels, not models: a model is just a graph, the inference engine is the scheduler/optimizer/executor, and the real work happens in MatMul, attention, RMSNorm, KV cache, quantized linear, and sampling kernels — especially fused kernels that avoid writing back to memory repeatedly.
Same model, same GPU, same VRAM can yield wildly different performance: optimized fused kernels make local inference feel impossibly fast, while a stack of 47 tiny kernel launches makes people blame the GPU. This is why inference engines and their kernel implementations matter.
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