    
    Independently Validated · University of Miami Frost Institute · Patents Pending

rolvsparse©

Cut AI energy costs by 99.9%.
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 133.5× real-world speedup on Llama-4 Maverick and 99.9% energy reduction. Real weights. Every platform. No hardware changes. No model retraining.

133.5×

Peak Real-World

Llama-4 Maverick · real weights · B200

83×

Production Serving

Claude 3.5-class · B=512 · B200

99.9%

Peak Energy Saved

Llama-4 Maverick · real weights

100.9×

Faster TTFT

Llama-4 400B · NVIDIA B200

Hardware Platforms

One library · one hash

Time-To-First-Token

100.9× faster TTFT

Llama-4 400B — TTFT speedup 100.9× on NVIDIA B200 with real weights. Llama-4 Maverick: 52.1× TTFT. GPT-4o class (B=512): 40.1× TTFT. Users experience near-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.

University of Miami↓ Validation Letter ↓ Validation Kit v2.0

01 — Throughput

Up to 133.5× faster on real-world frontier LLMs.

On NVIDIA B200, real Llama-4 Maverick MoE expert FFN weights deliver a 133.5× throughput gain — with 99.9% energy saved and 52.1× TTFT speedup. Llama-4 400B hits 125.3× speedup and 100.9× TTFT. DeepSeek-R1 delivers 44.2×. 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

NVIDIA B200 · PyTorch 2.8.0+cu128 · CUDA 12.8 · Batch 512 · 200 iterations

DeepSeek-R1 — All 256 MoE Experts Stacked Real weights · HuggingFace · CANONICAL ✓

up_proj · 256 experts × 2048×7168 → 524,288×7168 stacked · bfloat16 → fp32 · Sparsity 0.006% · Build time 0.11 s

cuBLAS

8.9k

rolvsparse©

704.4k

78.9×

Throughput

41.6×

TTFT Speedup

98.7%

Energy Saved

5,294

Eff. TFLOPS †

Energy: 106.90 J (rolv) vs 8,430.24 J · TTFT: 0.00140 s vs 0.05806 s
A_hash: 31575ec5d58089784332d7e1… · 4 shards · layers.3.mlp.experts.0–255 · ✓ CANONICAL · Hash-verified · Real weights from HuggingFace

133.5×

Llama-4 Maverick

Real weights from HuggingFace · NVIDIA B200 · 99.9% energy saved · 52.1× TTFT speedup.

125.3×

Llama-4 400B

Real weights · NVIDIA B200 · 99.4% energy saved · 100.9× TTFT speedup · 852K tokens/s.

44.2×

DeepSeek-R1

256 MoE experts · real weights · NVIDIA B200 · 98.7% energy saved · 41.6× TTFT.

83×

Claude 3.5 Serving

Architecture-matched · B=512 · NVIDIA B200 · 98.8% energy saved · 56.3× TTFT.

01b — Production Serving Benchmark

GPT-4o & Claude 3.5-class. The models running 80% of all API traffic.

We benchmarked the FFN layer at the architecture scale of GPT-4o and Claude 3.5 Sonnet across every batch size operators actually use — B=1 through B=512. The speedup increases as concurrency grows. At B=512 — where cuBLAS is fully optimised — ROLV delivers 68.7× (GPT-4o class) and 83× (Claude 3.5 class). Architecture-matched dimensions, synthetic fp32 weights (standard methodology for closed models). NVIDIA B200.

Batch	Serving context	GPT-4o Class speedup vs cuBLAS	Claude 3.5 Class speedup vs cuBLAS	GPT-4o p99 (ms)	Claude 3.5 p99 (ms)	Energy saved
1	Single user · SLA-critical	23.6×	36.3×	0.061	0.066	95–97%
4	Small burst	33.0×	59.7×	0.057	0.053	97–98%
16	Enterprise API	31.1×	61.2×	0.074	0.077	97–98%
64	High concurrency	38.8×	59.3×	0.075	0.088	97–98%
128	Heavy serving	52.1×	68.7×	0.100	0.134	98%
256	Datacenter batch	60.5×	77.5×	0.151	0.202	98–99%
512	Max throughput — cuBLAS comfort zone	68.7×	83.0×	0.252	0.360	98.5–98.8%

GPT-4o class: 8 experts × (18,432×7,168) = 147,456×7,168. Claude 3.5 class: 8 experts × (28,672×8,192) = 229,376×8,192. B=512 is where cuBLAS is fully optimised — large contiguous matmuls, saturated memory bandwidth. cuBLAS p99 at B=512: 16.6 ms (GPT-4o), 29.0 ms (Claude 3.5). ROLV canonical hash: 8dbe5f139fd946d4cd84e8cc…dad56dd8dd — identical across both architectures and all batch sizes ≥4.

83×

Peak serving speedup

Claude 3.5-class at B=512. Speedup grows with batch — ROLV scales better than cuBLAS under load.

98.8%

Energy saved

0.52 mJ/token vs 43.57 mJ/token at B=512. At 1B tokens/day: 12 kWh → 0.14 kWh per layer.

5,515

Eff. TFLOPS †

Claude 3.5-class at B=512. cuBLAS baseline: 66 TFLOPS. Build time: 54 ms, amortised. † Nominal dense FLOPs ÷ ROLV time — values above hardware peak reflect work reduction.

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.

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.

Model / Workload	Hardware	Speedup	Energy Saved	Tokens/sec (rolv)	TTFT Speedup
Llama-4 Maverick 400B real weights	NVIDIA B200	133.5×	99.9%	149,514	52.1×
Llama-4 400B real weights	NVIDIA B200	125.3×	99.4%	852,680	100.9×
DeepSeek-R1 · 256 MoE experts real weights	NVIDIA B200	44.2×	98.7%	704,363	41.6×
Mixtral-8×22B (56 layers)	NVIDIA B200	35.1×	98.2%	2,266,374	28.2×
Llama-3 70B FFN real weights	NVIDIA B200	50.5×	98.0%	7,179,519	—
Mistral-7B Wanda	NVIDIA B200	39.1×	97.4%	—	—
Mistral-7B Wanda	AMD MI300X	15.8×	93.7%	—	—
Qwen2.5-72B MoE FFN	NVIDIA B200	50.5×	91.4%	6,740,529	—
Kimi K2.5 (~1T MoE)	NVIDIA B200	10.5×	90.6%	490,929	29.7×
Qwen2.5-32B FFN real HF weights · 27,648×5,120	Google TPU v5e-8	5.9×	83.0%	3,924,124	—
Qwen3.5-35B-A3B-GPTQ-Int4 · 64 experts stacked real HF weights · GPTQ-Int4	NVIDIA B200	9.4×	89.3%	127,076,958	—
GLM-OCR · 24 layers stacked real HF weights · #1 OmniDocBench · 0.9B	NVIDIA B200	50.0×	98.0%	318,848,172	—
BERT-Base Real FFN real HF weights · 0% sparsity	Intel Xeon	12.3×	91.8%	103,801	—
GPT-4o Class · B=512 synthetic	NVIDIA B200	68.7×	98.5%	2,125,994	40.1×
Claude 3.5 Sonnet Class · B=512 synthetic	NVIDIA B200	83.0×	98.8%	1,467,584	56.3×

03 — 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.

The Democratization Argument

Intel Xeon + rolvsparse© vs. NVIDIA B200 — Full Comparison

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. cuSPARSE — NVIDIA's own sparse library — collapses at high sparsity and never competes.

Sparsity	Intel Xeon + rolvsparse©	NVIDIA B200 cuBLAS · no rolv	NVIDIA B200 cuSPARSE	Hardware Cost	Verdict
70%	~15,000	~80,000	~854	$2k vs $40k	GPU ahead
80%	~87,900	~80,000	~1,199	$2k vs $40k	$2k CPU overtakes $40k GPU
90%	~86,600	~80,000	~2,389	$2k vs $40k	rolv ahead; cuSPARSE collapses; 20× cheaper
95%	~80,000	~80,000	~5,044	$2k vs $40k	$2,000 CPU = $40,000 GPU
99%	~80,500	~80,000	~21,487	$2k vs $40k	rolv 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. Hardware cost: Intel ~$2,000 vs NVIDIA B200 ~$35,000–$40,000.

Mobile & EV — Edge AI

Battery life extension. +31.9% EV driving range.

rolvsparse© runs on-device — Android SoCs, automotive compute modules, embedded safety systems. No cloud dependency. No hardware swap. The same operator that accelerates frontier LLMs on NVIDIA B200 runs on a $200 phone chip and extends EV battery range by restructuring how sparse matrices are computed at the arithmetic level.

+31.9%

EV Driving Range

+44%

Mobile Battery Life

2.3×

EV Vision Safety

54.6%

Mobile Energy Saved

ViT-Base · Android

2.2× faster · 54.6% energy saved · on-device, no cloud.

EV Battery Management

2.1× faster · +33.4% range on identical hardware and battery.

Mobile SoC Avg.

2.42× avg. speedup · 56.6% energy reduction · +44.1% battery life.

04 — Benchmark Data

All real-world results. Downloads & live data.

Real model weights from HuggingFace for all open models. Architecture-matched synthetic fp32 used only for closed models (GPT-4o / Claude 3.5) — the standard methodology. All results available upon request with full methodology, hash verification, and independent validation below.

Independent Validation

University of Miami Frost Institute

Deterministic & reproducible results confirmed by independent academic institution across all tested hardware platforms. No commercial relationship.

↓ Validation Letter

Verification Kit v2.0

Run It Yourself

Run rolv-verifier.py on any hardware — any CPU, any laptop. Generate your own SHA-256 baseline hash and get a full "Us vs. Them" report.

↓ Validation Kit

Frontier-Scale MoE & Dense LLM Benchmarks REAL WEIGHTS · NVIDIA B200

Real model weights from HuggingFace where available. Architecture-matched synthetic fp32 for closed models (GPT-4o / Claude 3.5 Sonnet) — standard methodology.

Model	Matrix	Speedup	Energy Saved	Eff. TFLOPS †	Tokens/sec	TTFT Speedup
Llama-4 Maverick 400B	655,360×16384 · 128E	133.5×	99.9%	3,210.8	149,514	52.1×
Llama-4 400B	393,216×16384 · 8E	125.3×	99.4%	10,986.7	852,680	100.9×
Llama-4 Scout	40,960×16,384 · 16E	34.0×	98.8%	5,096.4	3,797,089	11.7×
DeepSeek-R1	524,288×7168 · 256E	44.2×	98.7%	5,294.1	704,363	41.6×
DeepSeek-V3	524,288×7168 · 256E	1.4×	98.7%	5,072.5	674,873	4.5×
Mixtral-8×22B (56 layers)	131,072×6144 · 8E	35.1×	98.2%	3,650.3	2,266,374	28.2×
Kimi K2.5 (~1T MoE)	786,432×896 · 384E	10.5×	90.6%	691.9	490,929	29.7×
Qwen3-235B (16E)	24,576×4096 · 16E	7.8×	95.5%	1,357.0	6,740,529	3.4×
Qwen3-235B (8E)	12,288×4096 · 8E	4.3×	93.7%	867.4	8,616,776	2.1×
GPT-4o Class · B=512 ★	147,456×7168 · 8E · synthetic	68.7×	98.5%	4,494.2	2,125,994	40.1×
Claude 3.5 Sonnet Class · B=512 ★	229,376×8192 · 8E · synthetic	83.0×	98.8%	5,515.3	1,467,584	56.3×

★ GPT-4o and Claude 3.5 Sonnet weights are not public — architecture-matched synthetic fp32, standard methodology. All other rows use real weights from HuggingFace. NVIDIA B200. Hash-verified.

† Effective TFLOPS explained: This column shows effective TFLOPS — computed as the nominal FLOPs of the equivalent dense matmul (2 × M × K × N) divided by ROLV's actual wall-clock time. When ROLV's result exceeds the hardware's theoretical peak (e.g. ~4.5 PFLOPS bfloat16 on B200), it means ROLV is doing far fewer multiply-accumulate operations than the dense baseline to produce the same output — not that the silicon is running faster than physics allows. The metric answers the question: how many dense-equivalent FLOPs per second is ROLV delivering? Values above hardware peak are proof of work reduction, not a measurement error.

Sparse, Pruned & Quantized Models

Real weights. Multiple hardware platforms. † Eff. TFLOPS = nominal dense FLOPs ÷ ROLV wall-clock time — values above hardware peak reflect work reduction, not a measurement error.

Model	Hardware	Sparsity	Speedup	Energy Saved	Eff. TFLOPS †	Tokens/sec
Llama-3 70B FFN	NVIDIA B200	50%	50.53×	98.0%	3,372.7	7,179,519
Mistral-7B Wanda	NVIDIA B200	55%	39.1×	97.4%	—	—
Llama-2-7B FFN	NVIDIA H100	70%	22.06×	95.5%	236.9	8,757,286
Mistral-7B Wanda	AMD MI300X	55%	15.8×	93.7%	—	—
Llama-3.1-8B FFN	Google TPU v5e-1	0%	8.4×	88.2%	—	12,902,131
Qwen2.5-32B FFN real HF weights	Google TPU v5e-8	0%	5.9×	83.0%	—	3,924,124
Qwen3.5-35B-A3B · 64 experts stacked real HF weights · GPTQ-Int4 · 81,920×512	NVIDIA B200	61.7%	9.4×	89.3%	10,659.99	127,076,958
GLM-OCR · 24 layers stacked real HF weights · gate_proj · 98,304×1,024	NVIDIA B200	0%	50.0×	98.0%	64,192.62 †	318,848,172
BERT-Large FFN	AMD MI300X	70%	4.84×	79.3%	26.6	10,578,270
BERT-Large FFN	NVIDIA H100	70%	3.55×	71.8%	39.5	15,694,435

CPU Benchmarks INTEL XEON

A $2,000 Intel Xeon system matches or beats a $40,000 NVIDIA B200 at ≥80% sparsity. Real model weights.

Model / Use Case	Sparsity	Speedup vs Dense	Energy Saved	Tokens/sec (ROLV)
GPT-J-6B FFN	40%	314.6×	99.7%	38,154
Mistral-7B FFN	0%	253.6×	99.6%	36,450
Llama-2-7B FFN	70%	169.2×	99.4%	43,116
Kimi K2.5 Expert Slice	—	40.3×	97.9%	84,413
BERT-Base FFN	0%	12.3×	91.8%	103,801
Finite Element Solver	80%	112.48×	99.1%	—

Speedup & Energy — All Workloads

Speedup (×) vs vendor best Energy saved (%) ★ Dense = 0% sparsity benchmark

Workload

Speedup

Energy saved

Synthetic Benchmarks — By Processor ONE TAB PER PLATFORM

Randomised and structured sparsity patterns on each hardware platform. Architecture-matched dimensions. Select a processor then a sparsity pattern.

Select processor

Select sparsity pattern

05 — 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 hash-verified across every platform.

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 SHA-256 output hashes published for independent third-party verification.

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

Frost Institute↓ Validation Letter Verification Kit↓ v2.0

No GPU Required

Try It Yourself — Any Hardware. Any Laptop.

Run our verification script on your own hardware and get a cryptographic SHA-256 fingerprint of the result. Email the JSON to rolv@rolv.ai — we run the same computation through rolvsparse© on identical inputs, produce the identical output hash, and return a full "Us vs. Them" comparison report showing your exact speedup and energy savings.

Step 1

Run the Script

Download and run rolv-verifier.py on your hardware. No GPU required — any CPU, any laptop. Novice users: paste into Jupyter and press Shift+Enter.

Step 2

Get Your Hash

The script outputs a SHA-256 fingerprint of your result — a cryptographic baseline unique to your hardware and run. It also captures full hardware specs and energy readings.

Step 3

Get Your Report

Email the .json file to rolv@rolv.ai. We run ROLV against your exact inputs and return a full comparison showing speedup, energy savings, and matching hash.

How SHA-256 Verification Works

The Baseline
Your hardware generates a unique SHA-256 fingerprint of the matrix result — produced entirely on your own machine.

The Match
ROLV processes the same data on our infrastructure and must produce the exact same hash — proving no math was skipped or precision reduced.

The Proof
Identical hash = identical precision. In rare cases of CUDA version drift we confirm numerically (atol=1e-5). The guarantee stands.

v2.0 — Real Hardware Energy Readings

NVIDIA GPUs
pynvml polls the GPU power rail every 50 ms; joules computed via trapezoidal integration of live readings.

AMD GPUs
pyrsmi provides equivalent live readings where the driver supports it. Falls back to estimate if unavailable.

CPU / Apple Silicon
Estimated from psutil CPU utilization × TDP — clearly labelled as an estimate in the output JSON via energy_measurement_method.

Recommended Cloud Environments

RunPod.io
NVIDIA & AMD GPU testing — A100, H100, B200, MI300X. Clean CUDA/ROCm stacks, accurate NVML/AMD SMI telemetry.

Google Cloud
AMD and Intel CPU instances — stable OS images, predictable performance, no power throttling. Ideal for EPYC benchmarks.

Google Colab
Intel Xeon CPU & Google TPU v5e-1 and v6e-1 — free tier available, standardised PyTorch/XLA environments.

Kaggle
Free Google TPU v5e-8 access — ideal for reproducing our TPU benchmarks. No credit card required.

System Requirements

PyTorch 2.5.0+ · CUDA 12.1 recommended · pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121
Output file format: rolv_baseline_<email>_<timestamp>.json — email to rolv@rolv.ai.

↓ Download Validation Kit v2.0

Academic Validation

University of Miami Frost Institute

The Frost Institute confirmed all rolvsparse© benchmarks as deterministic and reproducible on real hardware across every tested platform. No commercial interest. Engaged solely to verify accuracy and reproducibility.

↓ View Validation Letter →

Reproducibility

SHA-256 Hash-Verified · Cross-Platform

Identical numerical outputs confirmed on NVIDIA, AMD, Intel, TPU, and Apple hardware. The cryptographic hash 8dbe5f139fd946d4cd84e8cc…dad56dd8dd is the same across every platform and sparsity level.

↓ Download Verification Kit →

06 — 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 Type	Index Type	b	i	RSMT d	Use sparse when…
float32	int64	4	8	0.333	density < 33%
float16 / BF16	int64	2	8	0.200	density < 20%
float32	int32	4	4	0.500	density < 50%
int8	int32	1	4	0.200	density < 20%

RSMT Calculator

Model Size (GB)

Available VRAM (GB)

Value Bytes

Index Bytes

rolv Unit Calculator

Composite efficiency: (Sparsity × Energy Savings) / 100

Density D (0–1)

Energy Savings E (%)

07 — 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 openly.

Cut AI energy costs by 99.9%.rolv speeds up every AI chip — no new hardware.

Up to 133.5× faster on real-world frontier LLMs.

Llama 4 Maverick — MoE Expert FFN Real weights · HuggingFace

Qwen2.5-72B-Instruct — MoE Expert FFN

DeepSeek-R1 — All 256 MoE Experts Stacked Real weights · HuggingFace · CANONICAL ✓

GPT-4o & Claude 3.5-class. The models running 80% of all API traffic.

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

One library. Every chip. CPU beats flagship GPU.

Intel Xeon + rolvsparse© vs. NVIDIA B200 — Full Comparison

Battery life extension. +31.9% EV driving range.

All real-world results. Downloads & live data.

Frontier-Scale MoE & Dense LLM Benchmarks REAL WEIGHTS · NVIDIA B200

Sparse, Pruned & Quantized Models

CPU Benchmarks INTEL XEON

Speedup & Energy — All Workloads

Synthetic Benchmarks — By Processor ONE TAB PER PLATFORM

Every result is independently verified.

University of Miami — Frost Institute for Data Science and Computing

Try It Yourself — Any Hardware. Any Laptop.

University of Miami Frost Institute

SHA-256 Hash-Verified · Cross-Platform

The Rolv Sparse Memory Threshold: a universal rule.

The Founder.

Cut AI energy costs by 99.9%.
rolv speeds up every AI chip — no new hardware.