ROLVSPARSE© is a platform-agnostic, deterministic compute primitive that eliminates wasted Zero FLOPs — delivering orders-of-magnitude speedups and up to 99% energy savings. No new hardware. No retraining. No model changes.
ROLV delivers massive acceleration on commodity CPUs with no new hardware required. Validated on a standard Intel Xeon against the exact Kimi K2.5 expert FFN matrix (7168×2048, batch=512, ~87% sparsity).
Complete results across NVIDIA B200, AMD MI300X, Google TPU, Intel Xeon, and AMD EPYC at every sparsity level 0–99%. Validated by the University of Miami Frost Institute for Data Science and Computing.
| Sparsity | Metric | NVIDIA B200 | AMD MI300X | Google TPU | Intel Xeon | AMD EPYC |
|---|---|---|---|---|---|---|
| 0% | Speedup | 63.23× | 21.78× | 1.88× | 7.93× | 9.23× |
| Energy Saved | 98.42% | 95.41% | 46.67% | 87.40% | 89.17% | |
| 10% | Speedup | 63.22× | 21.88× | 1.79× | 7.69× | 9.32× |
| Energy Saved | 98.42% | 95.43% | 46–47% | 86.99% | 89.27% | |
| 20% | Speedup | 63.21× | 21.86× | 1.87× | 7.56× | 9.34× |
| Energy Saved | 98.42% | 95.39% | 46–47% | 86.77% | 89.29% | |
| 30% | Speedup | 63.22× | 21.75× | 1.77× | 7.54× | 9.15× |
| Energy Saved | 98.42% | 95.30% | 46–47% | 86.74% | 89.07% | |
| 40% | Speedup | 63.21× | 21.09× | 1.77× | 7.69× | 9.32× |
| Energy Saved | 98.42% | 95.20% | 46–47% | 87.00% | 89.27% | |
| 50% | Speedup | 63.18× | 20.88× | 1.77× | 7.40× | 9.23× |
| Energy Saved | 98.42% | 95.10% | 46–47% | 86.48% | 89.17% | |
| 60% | Speedup | 63.20× | 20.50× | 62.43× | 25.15× | 9.26× |
| Energy Saved | 98.42% | 95.35% | 98.40% | 96.02% | 89.20% | |
| 70% ▲ | Speedup | 243.07× | 242.19× | 51.16× | 40.57× | 9.24× |
| Energy Saved | 99.59% | 99.74% | 98.05% | 95.47% | 89.17% | |
| 80% | Speedup | 159.85× | 163.48× | 36.36× | 28.96× | 107.58× |
| Energy Saved | 99.37% | 99.50% | 97.25% | 94.51% | 99.07% | |
| 90% | Speedup | 79.05× | 84.56× | 16.71× | 12.72× | 116.67× |
| Energy Saved | 98.74% | 99.48% | 94.01% | 91.91% | 99.14% | |
| 95% | Speedup | 39.80× | 88.28× | 9.39× | 6.33× | 109.25× |
| Energy Saved | 97.49% | 99.45% | 89.35% | 83.89% | 99.08% | |
| 99% | Speedup | 8.27× | 35.00× | 2.41× | 1.87× | 95.93× |
| Energy Saved | 87.91% | 94.52% | 58.55% | 37.16% | 98.96% |
Baseline: Vendor dense library <70% sparsity · Vendor sparse library ≥70% sparsity · All results independently validated by the University of Miami Frost Institute · Full Benchmark PDF ↗
All production-scale workloads — real models, real datasets. Every result carries both speedup and energy savings — because ROLV eliminates the work entirely, not just speeds it up.
ROLV eliminates wasted Zero-FLOPs even in dense workloads — giving both speed and battery life gains on mobile SoCs and automotive compute.
| Camera AI — First Layer Vision | 2.82× | +50.4% battery |
| Always-On Audio DSP Filtering | 1.73× | +33.0% battery |
| On-Device AI Search (Embeddings) | 2.70× | +49.1% battery |
Overall: up to +44.1% increased battery life on the same phone.
| First-Layer Vision (Safety-Critical) | 2.30× | +36.7% range |
| Sensor Fusion & Kalman Filter | 1.65× | +25.6% range |
| Battery Management & Range Prediction | 2.06× | +33.4% range |
Overall: up to +31.9% increased driving range on the same battery.
Benchmarks independently validated. Deterministic and reproducible results confirmed across all tested platforms. Backend-agnostic reproducibility verified.
View Validation PDF →All benchmarks independently validated. Deterministic and reproducible results confirmed across all tested platforms. Nsight-validated tolerance harness included.
Download Validation Test PDF →Complete benchmark suite across NVIDIA, AMD, Intel, Google TPU, and Apple M-series. Synthetic and real-world production-scale workloads. Every result verifiable.
Download Benchmarks PDF →Memory capacity, not compute, is the true AI bottleneck. The RSMT identifies the exact density where sparse storage saves your model from VRAM exhaustion.
Rolv E. Heggenhougen, CEO of ROLV, LLC, is the founder of two public companies and technology companies across Norway, Sweden, Denmark, Latvia, Germany, Switzerland, Australia, China, and the U.S.
He spearheads the elimination of the Zero-FLOP bottleneck across global AI infrastructure with novel sparse matrix arithmetic paradigms — a compute primitive that works across GPUs, TPUs, CPUs, mobile SoCs, and next-generation accelerators with no changes to existing hardware or model stacks.
Mr. Heggenhougen has financed several start-up companies and holds deep cross-disciplinary expertise spanning AI compute architecture, patent law, and international technology commercialization.
He is fluent in Norwegian, Danish, and Swedish with working knowledge of German, a graduate of the University of Miami, attended Oslo University Law School, and is a certified pilot.