I ran four feed‑forward network (FFN) layers from real LLMs on a consumer HP All‑in‑One PC (Intel i7‑1165G7, 4 cores, 64 GB RAM). Each test compares: vendor de

I ran four feed‑forward network (FFN) layers from real LLMs on a consumer HP All‑in‑One PC (Intel i7‑1165G7, 4 cores, 64 GB RAM). Each test compares: vendor dense baseline vendor sparse baseline a custom sparse operator identical inputs identical weights identical precision SHA‑256‑verified outputs wall‑clock timing psutil‑based power measurement All baselines (dense and vendor sparse) run normally on this machine. The custom operator only changes runtime performance; it is not required to execute the models. Below are the raw results and full JSON for reproducibility. Mistral‑7B Wanda FFN (4096×14336 @ 55% sparsity) Speedup vs dense: 84.3× Speedup vs vendor sparse: 163.4× Energy reduction: 98.8% Tokens/s: 53,330 Dense TFLOPS: 0.07 Effective TFLOPS: 6.26 TTFT: 0.000590 s python { "benchmark": "Mistral-7B Wanda 55% Sparse FFN", "sparsity_pct": 55.0, "matrix": "4096x14336", "speedup_vs_dense_x": 84.3, "speedup_vs_sparse_x": 163.4, "energy_savings_pct": 98.8, "tokens_per_s_rolv": 53330.0, "tokens_per_s_dense": 633.0, "tokens_per_s_sparse": 326.0, "nominal_gflops_per_iter": 0.94, "dense_tflops": 0.07, "eff_tflops_rolv": 6.26, "ttft_s": 0.00059, "platform": "Intel Core i7-1165G7", "hardware": "4 cores — 63.7 GB RAM" } GPT‑J‑6B FFN (4096×16384 @ 40% sparsity) Speedup vs dense: 90.6× Speedup vs vendor sparse: 174.8× Energy reduction: 98.9% Tokens/s: 38,191 Dense TFLOPS: 0.06 Effective TFLOPS: 5.13 TTFT: 0.000387 s python { "benchmark": "GPT-J-6B 40% Sparse FFN", "sparsity_pct": 40.0, "matrix": "4096x16384", "speedup_vs_dense_x": 90.6, "speedup_vs_sparse_x": 174.8, "energy_savings_pct": 98.9, "tokens_per_s_rolv": 38191.0, "tokens_per_s_dense": 422.0, "tokens_per_s_sparse": 218.0, "nominal_gflops_per_iter": 1.074, "dense_tflops": 0.06, "eff_tflops_rolv": 5.13, "ttft_s": 0.000387, "platform": "Intel Core i7-1165G7", "hardware": "4 cores — 63.7 GB RAM" } Llama‑2‑7B FFN (4096×11008 @ 70% sparsity) Speedup vs dense: 87.4× Speedup vs vendor sparse: 116.1× Energy reduction: 98.9% Tokens/s: 73,916 Dense TFLOPS: 0.08 Effective TFLOPS: 6.67 TTFT: 0.000392 s python { "benchmark": "Llama-2-7B 70% Sparse FFN", "sparsity_pct": 70.0, "matrix": "4096x11008", "speedup_vs_dense_x": 87.4, "speedup_vs_sparse_x": 116.1, "energy_savings_pct": 98.9, "tokens_per_s_rolv": 73916.0, "tokens_per_s_dense": 845.0, "tokens_per_s_sparse": 637.0, "nominal_gflops_per_iter": 0.721, "dense_tflops": 0.08, "eff_tflops_rolv": 6.67, "ttft_s": 0.000392, "platform": "Intel Core i7-1165G7", "hardware": "4 cores — 63.7 GB RAM" } BERT‑Base FFN (3072×768, dense) Speedup vs dense: 4.8× Speedup vs vendor sparse: 23.9× Energy reduction: 79.0% Tokens/s: 104,131 Dense TFLOPS: 0.10 Effective TFLOPS: 0.49 TTFT: 0.000322 s python { "benchmark": "BERT-Base Real FFN", "sparsity_pct": 0.0, "matrix": "3072x768", "speedup_vs_dense_x": 4.8, "speedup_vs_sparse_x": 23.9, "energy_savings_pct": 79.0, "tokens_per_s_rolv": 104131.0, "tokens_per_s_dense": 21895.0, "tokens_per_s_sparse": 4349.0, "nominal_gflops_per_iter": 0.038, "dense_tflops": 0.10, "eff_tflops_rolv": 0.49, "ttft_s": 0.000322, "platform": "Intel Core i7-1165G7", "hardware": "4 cores — 63.7 GB RAM" } Notes All baselines (dense and vendor sparse) run on the same machine. “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. Power readings from psutil; not calibrated against external instrumentation. All outputs are SHA‑256‑verified to match dense results.