Contact Us

Run your models. Use less energy.

Explore a Pilot View Benchmarks

A new compute path for the models you already run.

Open-source LLM CB Converter CB LLM
Both networks use the same compact four-layer shape and fully dense connections. A narrow illumination band scans left to right across the original LLM. After a one-time conversion, the CB LLM uses the same left-to-right signal mechanism at a faster pace, with a blue glow distinguishing its active paths. The RooGenAI converter is a separate transformation marker, not part of the live signal flow.

RooGenAI’s new
Comparison-Based LLM algorithm.

  • A new computation path; not quantization.
  • A one-time conversion that completes quickly.
  • Runs on existing hardware infrastructure.

We took some of Google’s open-source LLMs and converted them into CB LLMs.See how we did.See how we did on converting one of Google's open-source LLMs into a CB LLM.

Gemma 4 12BOriginal LLM vs. CB LLM

Energy per token
57%
lower vs. the original LLM
Overall throughput
2.36×
four-workload geometric mean vs. the original LLM
Benchmark accuracy retained
99.6%
64.63% vs. 64.91% pooled MMLU
See How It Works See complete results, methodology, and tradeoffs

Pilot

Test RooGenAI before committing to adoption.

A reversible pilot for teams running LLM inference on accelerators that want to reduce energy use and infrastructure cost.

Explore the Pilot
  1. Step 1
    Select Model and Workload

    Freeze a representative baseline.

  2. Step 2
    Agree on Success Criteria

    Work with RooGenAI to define quality, energy, and performance targets for the pilot.

  3. Step 3
    RooGenAI Integration Support

    Convert, serialize, and measure side by side.

  4. Step 4
    Adopt After Proof

    Continue if the measured result meets your criteria, or stop cleanly.