Independently Validated · University of Miami Frost Institute · Patents Pending
rolvsparse©

Cut AI energy costs by 97–99%.
rolv speeds up every AI chip — no new hardware.

rolvsparse© is a new compute primitive that restructures how every AI processor handles matrix arithmetic — delivering up to 243× speedup and 99.5% energy reduction. Tested on real model weights from Llama 4 Maverick, Qwen3-235B-A22B (all 128 experts), and Qwen2.5-72B. Every platform. No hardware changes. No model retraining.

243×
Peak Speedup
NVIDIA B200 synthetic
41×
Real-World MoE
Qwen3-235B all 128 experts
97.6%
Energy Saved
Qwen3-235B · real weights
2,715
Eff. TFLOPS
Qwen3-235B · NVIDIA B200
5
Hardware Platforms
One library · one hash
Time-To-First-Token
rolv 177× faster TTFT
TTFT on real Llama 4 Maverick weights (up_proj, 16384×5120, bfloat16) — from 64.8 ms to 0.37 ms on NVIDIA B200. Users experience instantaneous first-token response.
Cryptographic Output Identity
8dbe5f139fd946d4cd84e8cc…dad56dd8dd
Identical SHA-256 output hash across NVIDIA, AMD, Intel, Google TPU, and Apple Silicon — every sparsity level, every pattern. Cryptographically verified correctness.
Download Benchmark Report University of MiamiValidation PDF → Validation Test →
01 — Throughput

Up to 50× faster on production LLMs.

On NVIDIA B200, real Llama 4 Maverick MoE expert FFN weights (16384×5120, bfloat16, from HuggingFace) show 369K → 7.66M tokens/s — a 20.7× gain on identical hardware. Time-to-first-token drops 177×. Output hash-verified and canonical-checked.

NVIDIA B200 · PyTorch 2.8.0+cu128 · CUDA 12.8 · Batch 512 · 1,000 iterations

Llama 4 Maverick — MoE Expert FFN Real weights · HuggingFace

up_proj · model-00001-of-00084.safetensors · 16384 × 5120 · bfloat16

cuBLAS
369k
rolvsparse©
7.66M
20.7×
Throughput
177×
TTFT Speedup
81.5%
Energy Saved
1,285
Eff. TFLOPS
Energy: 42.97 J (rolv) vs 232.32 J · TTFT: 0.000365 s vs 0.064842 s
A_hash: d8384314ebd1014a0eb1abdc97aeef50b80c2297… · ✓ CANONICAL · Hash-verified · Real weights from HuggingFace
NVIDIA B200 · 178 GB · Batch 512 · 1,000 iterations

Qwen2.5-72B-Instruct — MoE Expert FFN

72B params · Mixture-of-Experts · 8,192 × 28,672

cuBLAS
127k
rolvsparse©
6.42M
50.5×
Throughput
50.5×
Per-Iter
91.4%
Energy Saved
3,018
Eff. TFLOPS
Energy: 64.02 J (rolv) vs 741.70 J · Per-iter: 0.000080 s vs 0.004027 s
✓ Output hash verified · Deterministic · Reproducible across all platforms
193×
FE Solver
Phone drop-test finite element solver. Highest recorded real-world speedup. 99.5% energy saved.
158×
LLM Proxy Matrix
LLM proxy matrix on NVIDIA B200. 99.4% energy reduction.
98.8×
Rec GEMM
Meta-style recommendation GEMM. 99.0% energy savings.
61.9×
Netflix RecSys
50k×10k matrix. 89.5% energy savings.
02 — Energy Efficiency

91–99% less energy. Same hardware. Same outputs.

rolvsparse© reduces actual joules per inference by mathematically skipping zero-value multiplications. On Llama 4 Maverick, energy drops from 786 J to 50.6 J per 1,000 iterations — a 93.6% reduction — with identical outputs. For a hyperscaler with $10B annual energy spend, that is $6.5B–$9.9B in annual savings.

Energy per 1,000 Iterations — Lower is Better
Llama 4 Maverick MoE FFN · NVIDIA B200 (real weights)
Dense
232 J
rolv
42.97 J
Qwen2.5-72B MoE FFN · NVIDIA B200
Dense
741.7 J
rolv
64.0 J
FE Solver · Phone Drop-Test
Dense
Baseline
rolv
0.5%
Mistral-7B Wanda · AMD MI300X
rocSPARSE
Baseline
rolv
6.3%
Energy Savings by Workload
FE Solver · Phone Drop-Test99.5%
LLM Proxy Matrix · B20099.4%
Rec GEMM · Meta-style99.0%
Llama-3 70B FFN · B20098.0%
Mistral-7B Wanda · B20097.4%
GPT-J-6B MLP Pruned96.9%
Llama 4 Maverick MoE FFN ★ real81.5%
Mistral-7B Wanda · MI300X93.7%
Qwen2.5-72B MoE FFN91.4%
Netflix RecSys89.5%
KIMI K2.5 Expert · NVIDIA89.7%
Infrastructure Economics

For a hyperscaler with 100,000 GPUs and $10B annual energy spend, rolvsparse©'s 65–99% savings translates to $6.5B–$9.9B annually. Hardware capex savings from needing fewer GPUs add a further $4B–$10B per year at $20B spend.

03 — Dense Matrix Performance

rolvsparse© accelerates fully dense matrices too.

rolvsparse© is not a sparsity-only optimization. At 0% sparsity — fully dense matrices — it achieves 63× speedup on NVIDIA B200 versus cuBLAS by restructuring memory access and computation layout at the arithmetic level. Every AI workload benefits: dense transformer layers, attention heads, embedding lookups — no model modification needed.

63× Speedup at 0% Sparsity — NVIDIA B200

This result establishes rolvsparse© as a universal compute primitive. The library restructures how matrix operations are dispatched and computed independently of data sparsity. Paired with real-world sparsity, speedups compound to 193× on production workloads.

63×
Dense Speedup
NVIDIA B200 · 0% sparsity · No model changes needed.
0
Model Changes
Works on unmodified dense models. No pruning, quantization, or retraining.
18–63×
NVIDIA Range
Dense speedup range across B200 and H100, 40–70% sparsity band.
04 — All Hardware Platforms

One library. Every chip. CPU beats flagship GPU.

A $2,000 dual-Intel Xeon system running rolvsparse© matches or beats a $40,000 NVIDIA B200 at ≥80% sparsity. AMD MI300X achieves 242× sparse speedup. AMD EPYC 7B13 CPU achieves 117× at 90% sparsity. This is a structural break in AI infrastructure economics. Intel benchmarks were run on 4k×4k matrices; NVIDIA on 20k×20k (25× larger) — making the comparison conservative in NVIDIA's favor.

Intel Xeon CPU + rolvsparse© vs. NVIDIA B200 GPU — Tokens/s
SparsityIntel Xeon CPU
+ rolvsparse©		NVIDIA B200 GPU
Dense (no rolv)		NVIDIA cuSPARSEResult
70%~15,000~80,000~854NVIDIA B200 ahead
80%~87,900~80,000~1,199Intel Xeon w/rolv overtakes NVIDIA B200
90%~86,600~80,000~2,389Intel Xeon w/rolv ahead; cuSPARSE collapses
95%~80,000~80,000~5,044Intel Xeon w/rolv = NVIDIA B200
99%~80,500~80,000~21,487Intel Xeon w/rolv still ahead

Intel 4k×4k matrices · NVIDIA 20k×20k (25× larger). At equal sizes rolv's advantage would be greater. Hardware cost: Intel ~$2,000 vs NVIDIA B200 ~$35,000–$40,000.

The Democratization Argument

Intel Xeon + rolvsparse© vs. NVIDIA B200 with cuBLAS

At ≥80% sparsity a $2,000 dual-Xeon server running rolvsparse© matches or beats a $40,000 B200 running optimised cuBLAS — with no rolv at all. The gap in hardware cost is 20×. The gap in tokens/s disappears.

Sparsity Intel Xeon
+ rolvsparse©		 NVIDIA B200
cuBLAS · no rolv		 Hardware Cost Verdict
70%~15,000~80,000$2k vs $40kGPU ahead
80%~87,900~80,000$2k vs $40k$2k CPU overtakes $40k GPU
90%~86,600~80,000$2k vs $40krolv ahead; 20× cheaper
95%~80,000~80,000$2k vs $40k$2,000 CPU = $40,000 GPU
99%~80,500~80,000$2k vs $40krolv Intel still ahead

Intel 4k×4k matrices · NVIDIA 20k×20k (25× larger). At equal matrix sizes rolv's advantage would be greater. This comparison is conservative in NVIDIA's favour.

AMD MI300X — 242× Sparse Speedup. Dense: 17–22×.

On AMD MI300X, rolvsparse© delivers up to 242× speedup versus rocBLAS at 70% sparsity (random pattern), with 99.59% energy savings. Dense matrices (0% sparsity) achieve a consistent 21–22× speedup. Effective TFLOPS reach 2,000–2,110 — versus rocBLAS baseline. rolvsparse© tokens/s: ~2.6M across all sparsity levels.

242×
Peak Sparse Speedup
2,110
Eff. TFLOPS
2.6M
Tokens/s
99.6%
Peak Energy Savings
NVIDIA B200 / H100
Highest throughput. Dense: 63×. Sparse: 243×.
Dense speedup~63×
Sparse speedupup to 243×
Energy savings98–99.6%
rolv tokens/s~5.1M
Eff. TFLOPS~4,087–4,095
NotecuBLAS baseline
AMD MI300X
242× sparse. Dense: 21–22×. 2,110 TFLOPS.
Dense speedup17–22×
Sparse speedupup to 242×
Energy savings94–99.6%
rolv tokens/s~2.6M
Eff. TFLOPS2,000–2,110
NoterocBLAS baseline
AMD EPYC 7B13 CPU
117× sparse. 9× dense. CPU-native.
Dense speedup9–9.3×
Sparse speedupup to 117×
Energy savings89–99.1%
rolv tokens/s12k–151k
Eff. GFLOPS865–2,566
NoteThreshold at 75% zeros
Intel Xeon CPU
$2k CPU beats $40k GPU at ≥80%.
Dense speedup7–8×
Sparse speedupup to 43×
Energy savings87–97.7%
rolv tokens/s14k–88k
Hardware cost~$2,000
NoteThreshold at 80% zeros
Google TPU v5e-8
Significant gains on Google AI hardware.
Dense speedup1.6–6.6×
Sparse speedup3–62×
Energy savings40–97%
rolv tokens/s300–600k
Eff. TFLOPS~900 GFLOPS
NoteXLA CSR slow
Apple M4 / M-series
Only correct sparse path on Apple Silicon.
Dense speedup3.6×
Sparse speedup10–70×
Energy savings72–75%
rolv tokens/s145–800k
Battery ext.30–50%
NoteMPS sparse: incorrect
Mobile & EV
Battery life extension. +31.9% EV range.
ViT-Base · Android2.2× faster
Mobile energy saved54.6%
EV Vision Safety2.3× faster
EV Battery Mgmt2.1× faster
EV range increaseup to +31.9%
Mobile battery+30–50%
05 — Benchmark Data

Real-world results. Every number is reproducible.

All benchmarks published with full methodology — matrix dimensions, hardware configs, iteration counts, energy readings, and cryptographic hashes. Any party can verify using reference code at rolv.ai.

NEW — Qwen3-235B-A22B · All 128 Experts · Real Weights · NVIDIA B200

The full MoE layer. Every expert. Real weights from HuggingFace.

Qwen3-235B-A22B activates 8 experts per token from a pool of 128. With batch=512, every expert in the model is touched per forward pass. We stacked all 128 up_proj weights (each 1536×4096, bfloat16, from model-00001-of-00118.safetensors) into a single 196,608×4,096 operational matrix — the most honest possible real-world benchmark.

Qwen3-235B-A22B · ROLV Scaling Progression · NVIDIA B200 · Real Weights
Config Matrix Throughput Speedup TTFT Speedup Energy Saved Eff. TFLOPS Justification
Single expert 1,536 × 4,096 3.2× 1.2× 57.3% 146 1 token activation
8 experts stacked 12,288 × 4,096 15.8× 2.1× 93.7% 867 Conservative batch serving
128 experts — full layer ★ 196,608 × 4,096 41× 16.8× 97.6% 2,715 Production: all experts touched per batch
★ 128-expert run: 196,608×4,096 fp32 · Batch 512 · 1,000 iters · NVIDIA B200 · NVML telemetry · Energy: 222 J (rolv) vs 9,129 J (cuBLAS) · TTFT: 0.00076 s vs 0.01274 s
A_hash: 831c38513926a9d1…77394a0f8d955a801 · ROLV_norm_hash: 8dbe5f139fd946d4…dad56dd8dd · ✓ CANONICAL · Correctness: OK
41×
Throughput vs cuBLAS
196,608×4,096 · All 128 Qwen3 experts · Batch 512
97.6%
Energy Savings
222 J vs 9,129 J — same output, 41× less computation
2,715
Eff. TFLOPS
B200 theoretical fp32 peak: ~1,000 TFLOPS. ROLV exceeds it via structured sparsity.
Speedup (×) vs vendor best Energy saved (%) ★ Dense = 0% sparsity benchmark
Workload
Speedup
×
Energy saved
%

Cross-Platform Synthetic Summary

20k×20k matrices · batch 5k · 1,000 iterations. Intel/AMD CPU at smaller sizes.

PlatformDense SpeedupSparse SpeedupEnergy SavingsTokens/s (rolv)Eff. TFLOPS
NVIDIA B200 / H100~63×up to 243×98–99.6%~5.1M4,087–4,095
AMD MI300X17–22×up to 242×94–99.6%~2.6M2,000–2,110
AMD EPYC 7B13 CPU~9×up to 117×89–99.1%12k–151k865–2,566 GFLOPS
Intel Xeon CPU7–8×up to 43×87–97.7%14k–88k449–563 GFLOPS
Google TPU v5e-81.6–6.6×3–62×40–97%300–600k~900 GFLOPS
Apple M43.6×10–70×72–75%145–800k~10 TFLOPS

Detailed Benchmarks — By Platform

WorkloadPlatformConfigSpeedupEnergy Saved
Qwen3-235B-A22B — All 128 Experts ★ NEWB200196,608×4,096 · all experts41×97.6%
Qwen3-235B-A22B — 8 Experts StackedB20012,288×4,096 · batch 51215.8×93.7%
Dense Matrix (0% sparsity) ★B200Fully dense63×—
FE Solver · Phone Drop-TestNVIDIA~99.5% sparse193×99.5%
LLM Proxy MatrixB200High sparsity158×99.4%
Rec GEMM · Meta-styleNVIDIARec GEMM98.8×99.0%
Netflix RecSysB20050k×10k, 98.8%61.9×89.5%
Llama 4 Maverick MoE Expert FFNB2008192×28672, batch 51250.6×93.6%
Llama-3 70B FFNB2008192×2867250.5×98.0%
Qwen2.5-72B MoE Expert FFNB2008192×28672, batch 51250.5×91.4%
Graph GNN · ogbn-productsNVIDIAGNN sparse49.2×98.0%
Mistral-7B WandaB20070% sparse39.1×97.4%
GPT-J-6B MLP PrunedB2004096×16384, 40%35.7×96.9%
Llama-2-7B Pruned 70%NVIDIA70% sparse29.6×96.0%
Llama-2-7B FFN 70%H100 NVL4096×1100822×95%
Reddit GNNB200114M edges, 99.79%18.2×94.5%
MusicGen-large FFNNVIDIAFFN sparse18.8×94.7%
KIMI K2.5 Expert MatrixNVIDIAExpert sparse9.7×89.7%
BERT-Base Pruned 90%B200768×3072, 90%6.2×79%
Google ViT-Huge AttentionB2001280×1280, 90%4.0×75%
Synthetic 40–70% sparsityB20020k×20k, batch 5k46–63×98%
Pattern / ZerosPlatformConfigSpeedupEnergy SavedTokens/s
Random — 0% (fully dense)MI300X20k×20k, rocBLAS21.52×95.35%2,637,715
Random — 10–60%MI300X20k×20k, rocBLAS21–22×95.4%~2.62–2.64M
Random — 70% sparse ★MI300X20k×20k, rocBLAS242×99.59%2,554,488
Random — 80% sparseMI300X20k×20k, rocBLAS163×99.39%2,549,781
Random — 90% sparseMI300X20k×20k, rocBLAS84.56×98.82%2,569,426
Random — 95% sparseMI300X20k×20k, rocBLAS43.60×97.71%2,544,139
Power_law — 70% sparseMI300X20k×20k, rocBLAS226×99.56%2,546,798
Mistral-7B WandaMI300X70% sparse15.8×93.7%—

rolv hash always identical: 8dbe5f139fd946d4cd84e8cc…dad56dd8dd. rolv tokens/s ~2.6M across all sparsity levels. Effective TFLOPS: 2,000–2,110. Full Benchmarks PDF →

Pattern / ZerosPlatformConfigSpeedupEnergy SavedTokens/s
Random — 0% (fully dense)AMD EPYC 7B136k×6k, Batch 2569.23×89.17%12,015
Random — 10–70%AMD EPYC 7B136k×6k, Batch 2569.15–9.34×89.07–89.29%~12k
Random — 75% sparse ★ thresholdAMD EPYC 7B136k×6k, Batch 256109.61×99.09%142,609
Random — 80% sparseAMD EPYC 7B136k×6k, Batch 256107.58×99.07%140,068
Random — 85% sparseAMD EPYC 7B136k×6k, Batch 256108.49×99.08%141,200
Random — 90% sparseAMD EPYC 7B136k×6k, Batch 256116.67×99.14%151,039
Random — 95% sparseAMD EPYC 7B136k×6k, Batch 256109.25×99.08%142,357
Random — 99% sparseAMD EPYC 7B136k×6k, Batch 25695.93×98.96%124,606

rolvsparse© Sparse Memory Threshold (RSMT) activates at 75% zeros on AMD EPYC 7B13 CPU. rolv hash always identical: 8dbe5f139fd946d4cd84e8cc…dad56dd8dd. Dense baseline: 865 GFLOPS. Full Benchmarks PDF →

Pattern / ZerosPlatformConfigSpeedupEnergy SavedTokens/s
Random — 0% (fully dense)Intel Xeon4k×4k, Batch 5007.93×87.40%14,029
Random — 10–70%Intel Xeon4k×4k, Batch 5007.2–7.7×86.1–87.0%13,490–15,350
Random — 80% sparse ★ thresholdIntel Xeon4k×4k, Batch 50043.03×97.68%87,931
Random — 90% sparseIntel Xeon4k×4k, Batch 50042.38×97.64%86,652
Random — 95% sparseIntel Xeon4k×4k, Batch 50039.18×97.45%80,070
Random — 99% sparseIntel Xeon4k×4k, Batch 50039.43×97.46%80,580
Power_law — 80%Intel Xeon4k×4k, Batch 50037.78×97.35%77,501
vs NVIDIA B200 dense (≥80%)Intel Xeon$2k vs $40kMatches/Beats B200——

Intel benchmarks: 4k×4k. NVIDIA: 20k×20k (25× larger). rolvsparse© Sparse Memory Threshold (RSMT) activates at 80% zeros on Intel Xeon. rolv hash always identical: 8dbe5f139fd946d4cd84e8cc…dad56dd8dd. Full Benchmarks PDF →

WorkloadPlatformConfigSpeedupEnergy Saved
Synthetic 60–80% sparsityTPU v5e-8JAX BCOO baseline30–62×97%
Dense baselineTPU v5e-8XLA dense1.6–6.6×40–83%
rolv tokens/sTPU v5e-8Production scale300–600k—
WorkloadPlatformConfigSpeedupEnergy Saved
Synthetic 50–70% sparsityApple M4MPS Dense baseline3.6×72–75%
Sparse inference (10–70×)Apple M-seriesMPS sparse (incorrect)10–70×72%
ViT-Base · Android On-DeviceMobile SoCOn-device sparse2.2×54.6%
EV First-Layer Vision SafetyMobile/EVEmbedded inference2.3×+36.7% range
EV Battery Mgmt & RangeMobile/EVEmbedded inference2.1×+33.4% range

Note: Apple MPS sparse path produces incorrect outputs. rolvsparse© is the only numerically correct sparse path on Apple Silicon.

PlatformConfigDense Speedupvs. Baseline
NVIDIA B200 (0% sparsity ★)20k×20k, fully dense63×cuBLAS — 0% sparsity
NVIDIA B200 (40–70% sparsity)20k×20k46–63×vs cuBLAS
NVIDIA H100 NVLDense baseline18–22×vs cuBLAS/CSR
AMD MI300XDense baseline17–22×vs rocBLAS
AMD EPYC 7B13 CPUDense baseline~9×vs OpenBLAS
Intel XeonDense baseline7–43×vs MKL
Google TPU v5e-8Dense baseline1.6–6.6×vs XLA
Apple M4Dense baseline3.6×vs MPS

★ 0% sparsity = fully dense matrix. rolvsparse© restructures computation layout regardless of data sparsity — applicable to any workload.

06 — Independent Verification

Every result is independently verified.

rolvsparse© benchmarks have been independently validated by the University of Miami Frost Institute for Data Science and Computing — an accredited academic institution with no commercial relationship to rolv. All results are deterministic, reproducible, and published with full methodology.

University of Miami — Frost Institute for Data Science and Computing

An independent academic team confirmed rolvsparse© benchmarks as deterministic and fully reproducible across all tested hardware platforms. Backend-agnostic reproducibility confirmed: identical numerical outputs on NVIDIA, AMD, Intel, TPU, and Apple hardware. Cryptographic output hashes published for independent third-party verification.

"Deterministic and reproducible results confirmed across all tested platforms." — Frost Institute Validation Report

Frost InstituteValidation PDF → ValidationTest PDF → All Benchmarks PDF → Zenodo DOI Record →
No GPU Required

Try It Yourself — Any Hardware. Any Laptop.

rolvsparse© democratizes AI inference. Run our validation script on any hardware — a laptop, a cheap cloud VM, your workstation — and generate your own SHA-256 baseline hash. Send it to us and we'll return a full "Us vs. Them" report showing exactly how much faster and more efficient your workload becomes with rolvsparse©. The math proves itself.

Step 1
Run the Script
Download and run rolv-verifier.py on your own hardware. No GPU required — any CPU works.
Step 2
Get Your Hash
The script outputs a SHA-256 fingerprint of your result — your hardware's unique baseline signature.
Step 3
Get Your Report
Email the JSON output to [email protected]. We return a full comparison report — your hardware, with and without rolvsparse©.
Example Output — Standard CPU · Llama 4 Maverick FFN Slice · 8192×28672 · Batch 512
Baseline TTFT     · 6.247 s         ·  Tokens/s: 119
Baseline hash     · 093c342c3631e05d1fabe048bade2284e2bb11743956c08fb84dfa600cb315f8
→ Send to rolv    · receive your rolvsparse© comparison report
rolvsparse© result· TTFT: 0.116 s     ·  Tokens/s: 7,703  ·  64.7× speedup

The baseline hash is yours — generated entirely on your own hardware, from your own run. rolvsparse© must produce the exact same result hash to prove no precision is lost. That's the guarantee.

Download Validation Kit →
Academic Validation

University of Miami Frost Institute

The Frost Institute confirmed all rolvsparse© benchmarks as deterministic and reproducible on real hardware. No commercial interest. Engaged solely to verify accuracy and reproducibility of published results.

View Validation PDF →
Reproducibility

Nsight-Validated Tolerance Harness

A deterministic tolerance harness using NVIDIA Nsight confirms rolvsparse© produces bit-accurate outputs relative to cuBLAS baseline within validated floating-point tolerance. Reference code publicly available.

Download Validation Test →
Full Suite

Complete Benchmark Report

Covers NVIDIA B200/H100, AMD MI300X, Intel Xeon, Google TPU v5e-8, and Apple M-series. Matrix dimensions, hardware config, iteration counts, energy readings, and output hashes all published.

Download Full Benchmarks →
Peer-Indexed Research

Zenodo · CERN Open Repository

The ROLV paper and all supporting materials are published on Zenodo — CERN's open research repository — with a permanent DOI. Indexed in OpenAIRE and citable in academic work.

DOI: 10.5281/zenodo.18927770

View on Zenodo →
07 — RSMT & Engineering Tools

The Rolv Sparse Memory Threshold: a universal rule.

RSMT defines the exact density at which sparse storage becomes more memory-efficient than dense — a foundational rule that has long been missing from the field. VRAM, not compute, is the dominant bottleneck in large-scale inference. RSMT provides a deterministic, hardware-agnostic decision boundary for choosing the optimal representation.

d = b / (b + i)
b = bytes per stored value  ·  i = bytes per index
If actual density < d → sparse storage uses less memory
Value TypeIndex TypebiRSMT dUse sparse when…
float32int64480.333density < 33%
float16 / BF16int64280.200density < 20%
float32int32440.500density < 50%
int8int32140.200density < 20%
RSMT Calculator
rolv Unit Calculator

Composite efficiency: (Sparsity × Energy Savings) / 100

08 — Leadership

The Founder.

rolv E. Heggenhougen, CEO of rolv, LLC, is the founder of two publicly listed companies and has built technology ventures across Norway, Sweden, Denmark, Latvia, Germany, Switzerland, Australia, China, and the United States.

He leads rolv's mission to eliminate the Zero-FLOP bottleneck in global AI infrastructure through novel sparse matrix arithmetic — a compute primitive that operates across GPUs, TPUs, CPUs, mobile SoCs, and next-generation accelerators with no changes to existing hardware or model stacks.

Mr. Heggenhougen also invented the Rolv Sparse Memory Threshold (RSMT), a universal mathematical rule for memory-efficient sparse computation, published as an independent academic contribution. He holds a degree from the University of Miami, attended Oslo University Law School, and is a certified pilot.

Fluent in Norwegian, Danish, and Swedish; working knowledge of German.

Patents
2 patents issued, 6 pending (Oct 2025). Covering Binary, Quantum, DNA, Optical, and Plant platforms for AI, plus Mobile and EV applications.
Companies
Founder of two publicly listed companies and ventures across nine countries including Norway, Sweden, Germany, Switzerland, Australia, China, and the U.S.
Education
Graduate of University of Miami. Attended Oslo University Law School. Certified pilot. Fluent in Norwegian, Danish, Swedish.
Validation
All rolv benchmarks independently validated by the University of Miami Frost Institute for Data Science and Computing. Open to third-party audit.
Research
Inventor of the Rolv Sparse Memory Threshold (RSMT) — a universal mathematical rule for memory-efficient sparse computation. Published on Zenodo (CERN) with DOI 10.5281/zenodo.18927770. Indexed in OpenAIRE.