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<title>Genrius AI</title>
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  <h1>Genrius AI</h1>
  <p class="tag">Generating Real Intelligence. We build AI models and make them small and fast enough to run on hardware people already own.</p>
  <h2>Pingala-8: 2-bit LLMs for llama.cpp, no retraining</h2>
  <ul>
    <li>85% smaller: Qwen3-4B goes from 8.05 GB to 1.24 GB, and Llama 3.1 8B from 16.08 GB to 2.39 GB.</li>
    <li>At the file size of llama.cpp's IQ2_XXS, our Qwen3-4B reaches WikiText-2 perplexity 16.64. IQ2_XXS reaches 28.61 and the best IQ2_KT file we built reaches 18.15 (lower is better).</li>
    <li>Where we lose: Qwen3-14B (IQ2_KT 9.40, ours 9.57), prompt processing (0.74x of IQ2_XXS), and retrained models such as Ternary Bonsai 4B.</li>
  </ul>
  <img src="pingala8-poster.jpg" alt="Pingala-8 Qwen3-4B (1.24 GB) answering in a local chat window on an RTX 5070 Ti Laptop GPU">
  <h2>Links</h2>
  <ul>
    <li><a href="https://github.com/Genrius-AI/pingala8-llm/releases/tag/v2.4-oneclick" target="_blank" rel="noopener noreferrer">One-click Windows build</a></li>
    <li><a href="https://github.com/Genrius-AI/pingala8-llm" target="_blank" rel="noopener noreferrer">Code</a></li>
    <li><a href="https://doi.org/10.5281/zenodo.23481757" target="_blank" rel="noopener noreferrer">Paper</a></li>
    <li><a href="https://huggingface.co/Genrius-AI/Qwen3-4B-Pingala8-2bit" target="_blank" rel="noopener noreferrer">Qwen3-4B-Pingala8-2bit</a> and <a href="https://huggingface.co/Genrius-AI/Llama-3.1-8B-Pingala8-2bit" target="_blank" rel="noopener noreferrer">Llama-3.1-8B-Pingala8-2bit</a></li>
    <li><a href="https://huggingface.co/datasets/Genrius-AI/pingala8-benchmarks" target="_blank" rel="noopener noreferrer">Benchmark tables</a></li>
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