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).
There are billions of CPUs already deployed across data centers, enterprise servers, edge devices, and consumer hardware. With ROLV, that installed base — quietly humming along running the world’s software — becomes the largest high-performance AI compute cluster ever assembled. No procurement. No shipping. No waiting. The hardware already exists; ROLV simply unlocks it.
For the first time, the same compute primitive that accelerates a hyperscaler’s 100,000-GPU supercluster runs identically — and transformatively — on the chip inside your pocket. ROLV democratizes AI inference across the full spectrum of hardware: from flagship smartphones and electric vehicles to on-premise servers and the largest AI factories on earth. One primitive. Every platform. No trade-offs.
Best per-sparsity results across NVIDIA B200, AMD MI300X, Google TPU, Intel Xeon, AMD EPYC, and Apple M4. All validated by the University of Miami Frost Institute for Data Science and Computing. Compared vs. native vendor libraries (cuBLAS, cuSPARSE, ROCm, XLA, MKL).
Dense < 70% sparsity · Sparse ≥ 70% sparsity
| Sparsity | Metric | NVIDIA B200 | AMD MI300X | Google TPU | Intel Xeon | AMD EPYC | Apple M4 | Verify |
|---|---|---|---|---|---|---|---|---|
| 0% | Speedup / Energy | 63.23× / 98.4% | 21.78× / 95.4% | 1.88× / 46.7% | 7.93× / 87.4% | 9.23× / 89.2% | 3.60× / 72.6% | PDF ↗ |
| 10% | Speedup / Energy | 63.22× / 98.4% | 21.88× / 95.4% | 1.79× / 46–47% | 7.69× / 87.0% | 9.32× / 89.3% | 3.60× / 72.6% | PDF ↗ |
| 20% | Speedup / Energy | 63.21× / 98.4% | 21.86× / 95.4% | 1.87× / 46–47% | 7.56× / 86.8% | 9.34× / 89.3% | 3.60× / 72.6% | PDF ↗ |
| 30% | Speedup / Energy | 63.22× / 98.4% | 21.75× / 95.3% | 1.77× / 46–47% | 7.54× / 86.7% | 9.15× / 89.1% | 3.60× / 72.6% | PDF ↗ |
| 40% | Speedup / Energy | 63.21× / 98.4% | 21.09× / 95.2% | 1.77× / 46–47% | 7.69× / 87.0% | 9.32× / 89.3% | 3.60× / 72.6% | PDF ↗ |
| 50% | Speedup / Energy | 63.18× / 98.4% | 20.88× / 95.1% | 1.77× / 46–47% | 7.40× / 86.5% | 9.23× / 89.2% | 3.60× / 72.6% | PDF ↗ |
| 60% | Speedup / Energy | 63.20× / 98.4% | 20.50× / 95.4% | 62.43× / 98.4% | 25.15× / 96.0% | 9.26× / 89.2% | 3.60× / 72.6% | PDF ↗ |
| 70% ★ | Speedup / Energy | 243.07× / 99.6% | 242.19× / 99.7% | 51.16× / 98.1% | 40.57× / 95.5% | 9.24× / 89.2% | 3.60× / 72.6% | PDF ↗ |
| 80% | Speedup / Energy | 159.85× / 99.4% | 163.48× / 99.5% | 36.36× / 97.3% | 28.96× / 94.5% | 107.58× / 99.1% | 3.60× / 72.6% | PDF ↗ |
| 90% | Speedup / Energy | 79.05× / 98.7% | 84.56× / 99.5% | 16.71× / 94.0% | 12.72× / 91.9% | 116.67× / 99.1% | 3.60× / 72.6% | PDF ↗ |
| 95% | Speedup / Energy | 39.80× / 97.5% | 88.28× / 99.5% | 9.39× / 89.4% | 6.33× / 83.9% | 109.25× / 99.1% | 3.60× / 72.6% | PDF ↗ |
| 99% | Speedup / Energy | 8.27× / 87.9% | 35.00× / 94.5% | 2.41× / 58.6% | 1.87× / 37.2% | 95.93× / 99.0% | 3.60× / 72.6% | PDF ↗ |
★ Peak sparse region begins at 70% sparsity. All results independently validated. View Frost Institute PDF ↗
All production-scale workloads — real models, real datasets. Sorted by per-iteration speedup. Nsight-validated results included where noted.
ROLV eliminates wasted Zero-FLOPs even in dense workloads — giving both speed and battery life gains on mobile SoCs and automotive compute. Measured on NVIDIA B200 (best proxy for 2026 flagship hardware).
| 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. If you own a Tesla with Grok, ROLV would speed up Grok responses while improving energy efficiency.
Benchmarks independently validated. Deterministic and reproducible results confirmed across all tested platforms. Backend-agnostic reproducibility verified.
View Validation PDF →Run benchmarks in minutes. Hash-verified outputs. Identical normalized results across every architecture — verify every claim yourself. Nsight-validated tolerance harness included.
github.com/rolvai/rolv-verifier →Complete benchmark suite across NVIDIA, AMD, Intel, Google TPU, and Apple M-series. Synthetic and real-world production-scale workloads. Every result linked and 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 and performance degradation.
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.