ROLVSPARSE© is a platform-agnostic, deterministic compute primitive that eliminates wasted Zero FLOPs — delivering orders-of-magnitude speedups and up to 99% energy savings across every GPU, CPU, TPU, and mobile SoC. No new hardware required.
FE Solver, 80% sparse — Intel Xeon
vs CSR Sparse on same workload
Planetary-scale AI inference
Beats cuBLAS, cuSparse & ROCm
The world has billions of CPUs already deployed in servers, workstations, and edge devices. Standard sparse libraries like MKL CSR — supposedly optimised for exactly this — are still 112× slower than ROLVSPARSE© on an 80% sparse FE Solver workload.
And on Kimi K2.5 expert slices (~87% sparse), ROLVSPARSE© achieves 40× acceleration on those same commodity Xeon CPUs — turning the global CPU installed base into the largest AI inference network ever built.
This is the headline that changes everything. ROLVSPARSE© achieves 63× speedup on an NVIDIA B200 — the most advanced GPU on the planet — at zero percent sparsity. No sparse structure to exploit. Pure Zero FLOP elimination doing work that every other library leaves on the table.
cuBLAS, cuSparse, and ROCm are all beaten. This isn't a sparse trick — it's a fundamental improvement to how floating-point operations are executed at the primitive level.
Modern AI models — LLMs, recommendation systems, graph networks — are 50–99% sparse. The vast majority of matrix values are zero.
Standard compute libraries like cuBLAS and MKL process every value. They multiply every zero, burn every watt, waste every cycle.
ROLVSPARSE© eliminates Zero FLOPs at the primitive level. No new silicon. Deterministic. Verifiable.
Verified · Deterministic · Platform-Agnostic
Mobile chassis drop-test, 80% sparse. ROLV: 0.000476s. MKL CSR: 0.053517s. 99.1% energy saved.
Beats cuBLAS, cuSparse & ROCm. No sparse structure needed — pure Zero FLOP elimination at the primitive level.
Nsight Compute verified. 99.37% energy saved. Demonstrates ROLVSPARSE© on real LLM proxy workloads.
On Kimi K2.5 expert slices (~87% sparse). Turns the global CPU base into the world's largest AI inference network.
Across hundreds of workloads. Slashes CapEx and OpEx for every AI deployment — mobile to hyperscale.
Camera AI +2.82×, audio DSP +1.73×, on-device search +2.7×. EVs: faster sensor fusion & vision AI.
Planetary-scale AI inference. Global throughput across cloud, edge, and on-device simultaneously.
Identical normalized outputs across all architectures. Hash-verified every run. Verify in minutes with the open-source kit.
Patents filed across binary, quantum, DNA, optical, and plant-based AI computing. Every paradigm covered.
Independently validated by the University of Miami Frost Institute. All results deterministic and hash-verified. Run them yourself.
| Workload | Platform | Sparsity | Speedup | Energy Saved |
|---|---|---|---|---|
| FE Solver — Mobile Chassis (vs CSR Sparse MKL) | Intel Xeon | 80% | 112.48× | 99.1% |
| LLM Proxy Matrix 4096×5120 (Nsight Compute) | NVIDIA GPU | Variable | 158× | 99.37% |
| Large Recommendation GEMM (32k×32k) | NVIDIA B200 | High | 98× | 99.0% |
| FE Solver — Mobile Chassis (vs Dense PyTorch) | Intel Xeon | 80% | 49.85× | 98.0% |
| Netflix RecSys Subsample | NVIDIA GPU | ~95% | 61× | 89.5% |
| Llama-3 70B FFN Layer | NVIDIA B200 | 50% | 50× | 98.0% |
| Stanford OGB ogbn-products Graph | NVIDIA GPU | 80% | 49× | 98.0% |
| Mistral-7B Wanda Pruned | AMD MI300X | 55% | 15.8× | 93.7% |
| Taobao Ads Recommender | CPU | >99.999% | 2× | 52.3% |
Full suite: rolv.ai/benchmarks · Validated by University of Miami Frost Institute for Data Science and Computing
ROLVSPARSE© accelerates on-device AI across every major mobile workload without new chips or firmware changes.
First-layer vision, sensor fusion, and range prediction all accelerated on existing automotive SoCs — no hardware change.
Platform-agnostic by design. One primitive across all hardware — identical deterministic outputs everywhere. No vendor lock-in.
"ROLVSPARSE© doesn't just accelerate AI — it reduces energy consumption, democratizes compute, and makes any device an ultra-efficient AI engine."Rolv E. Heggenhougen — Founder, ROLV LLC
Three decades of deep technology innovation — from founding and scaling companies globally to building what may be the most impactful compute primitive since BLAS.
Benchmarks independently validated. Deterministic and reproducible results confirmed across all tested platforms.
View Validation PDF → 02 Open SourceRun benchmarks in minutes. Hash-verified outputs. Identical normalized results across every architecture — verify every claim yourself.
github.com/rolv-ai → 03 Full SuiteSynthetic and real-world benchmarks across NVIDIA, AMD, Intel, Google TPU, and Apple M-series. Every result linked and verifiable.
rolv.ai/benchmarks →