Methodology and reproducibility details: All benchmarks were run on the same machine: HP All‑in‑One, Intel i7‑1165G7 (4 cores), 64 GB RAM. All tests use ident

Methodology and reproducibility details: All benchmarks were run on the same machine: HP All‑in‑One, Intel i7‑1165G7 (4 cores), 64 GB RAM. All tests use identical inputs, identical weights, identical precision, and identical batch size. Dense baseline uses the system BLAS (MKL/oneDNN depending on environment). Vendor sparse baseline uses standard CSR/COO kernels. The custom sparse operator runs in the same Python environment and on the same CPU. All baselines (dense and vendor sparse) run normally on this hardware; the custom operator only changes runtime performance, not model executability. Wall‑clock time is measured with time.perf_counter() around the matmul call. Power readings come from psutil.sensors_battery() and psutil.cpu_freq(); these are not calibrated against external instrumentation. “Effective TFLOPS” = nominal dense FLOPs ÷ wall‑clock time. Values above hardware peak indicate fewer multiply‑accumulate operations executed than dense. Dense TFLOPS is the actual hardware utilization number. “Tokens/s” is computed as 1 ÷ (per‑iteration wall‑clock time). TTFT is measured as the time from operator invocation to first output. All outputs are SHA‑256‑verified to match dense results bit‑for‑bit. No quantization, no weight modification, and no model retraining were used. All JSON blocks in the post are the raw outputs from the benchmark script.