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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Pingala-8 2-bit LLMs</title>
<meta name="description" content="Pingala-8 by Genrius AI: a 2-bit GGUF format for llama.cpp. Qwen3-4B goes from 8.05 GB to 1.24 GB (85% smaller), with WikiText-2 perplexity 16.64 against 28.61 for llama.cpp IQ2_XXS at the same size. No retraining. One-click Windows build.">
<meta property="og:title" content="Pingala-8: 2-bit LLMs for llama.cpp, 85% smaller">
<meta property="og:description" content="Same 1.24 GB as llama.cpp's 2-bit, much better quality. No retraining. One-click Windows build for NVIDIA RTX 30/40/50.">
<meta property="og:image" content="https://huggingface.co/spaces/Genrius-AI/pingala8/resolve/main/poster.jpg">
<meta property="og:type" content="website">
<meta name="twitter:card" content="summary_large_image">
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</style>
</head>
<body>
<div class="glow" aria-hidden="true"></div>

<header>
  <div class="wrap bar">
    <a class="brand" href="https://huggingface.co/Genrius-AI" target="_blank" rel="noopener"><span class="mark">G</span>Genrius AI</a>
    <nav aria-label="Sections">
      <a class="opt" href="#results">Results</a>
      <a class="opt" href="#limits">Limits</a>
      <a href="#try">Try it</a>
      <a href="#models">Models</a>
      <a href="https://doi.org/10.5281/zenodo.23481757" target="_blank" rel="noopener">Paper</a>
    </nav>
  </div>
</header>

<main>
<div class="wrap">

  <div class="hero">
    <span class="eyebrow"><i></i>Pingala-8 &middot; 2-bit GGUF for llama.cpp</span>
    <h1>85% smaller LLMs. <em>Better than llama.cpp's own <span class="nw">2-bit</span></em> at the same size.</h1>
    <p class="lede">We compress models you already have to 2 bits, with <b>no retraining</b>. Qwen3-4B goes from <b>8.05 GB to 1.24 GB</b>, and at that size its WikiText-2 perplexity is <b>16.64 against 28.61</b> for llama.cpp's IQ2_XXS (lower is better).</p>
    <div class="cta">
      <a class="btn primary" href="https://github.com/Genrius-AI/pingala8-llm/releases/latest" target="_blank" rel="noopener">
        <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M12 3v12"/><path d="m7 10 5 5 5-5"/><path d="M5 21h14"/></svg>
        Download the one-click build
      </a>
      <a class="btn" href="#models">See the models</a>
      <a class="btn" href="https://doi.org/10.5281/zenodo.23481757" target="_blank" rel="noopener">Read the paper</a>
    </div>
    <p class="fine">Windows and Linux &middot; NVIDIA RTX 30/40/50 &middot; runs on our patched llama.cpp (CUDA)</p>
  </div>

  <div class="video">
    <video src="demo.mp4" poster="poster.jpg" controls muted autoplay loop playsinline preload="metadata" aria-label="Pingala-8 Qwen3-4B writing a short email in real time"></video>
  </div>
  <p class="cap">Real time, not sped up. Text and timing come from a real run of Pingala-8 Qwen3-4B (1.24 GB) on an RTX 5070 Ti Laptop GPU; the chat window is recreated.</p>

  <div class="stats">
    <div class="stat">
      <div class="n">85%</div>
      <div class="l">smaller</div>
      <p>Qwen3-4B: 8.05 GB fp16 to 1.24 GB. Llama-3.1-8B: 16.08 GB to 2.39 GB.</p>
    </div>
    <div class="stat">
      <div class="n">16.64</div>
      <div class="l">perplexity at 1.24 GB</div>
      <p>vs 28.61 for IQ2_XXS and 18.15 for IQ2_KT at the same size (Qwen3-4B, WikiText-2, lower is better).</p>
    </div>
    <div class="stat">
      <div class="n">+21%</div>
      <div class="l">quiz average vs IQ2_XXS</div>
      <p>50.8 vs 41.9 over five zero-shot tests, keeping 90% of the 8-bit original's 56.3.</p>
    </div>
    <div class="stat">
      <div class="n">1.10x</div>
      <div class="l">generation speed vs IQ2_XXS</div>
      <p>Batch 1. Also 39% faster than Q4_K_M at half its size. Reading prompts is slower (0.74x).</p>
    </div>
  </div>

  <!-- RESULTS -->
  <section id="results">
    <p class="kicker">Results</p>
    <h2>Same bytes, better model</h2>
    <p class="sub">Every number on this page comes from the paper and the logs in the code repository. Our shipped file is called <b>B4</b> in the chart.</p>

    <figure class="panel" style="margin:0">
      <div class="chart"><a href="ppl_vs_size.png" target="_blank" rel="noopener" title="Open the chart full size"><img src="ppl_vs_size.png" alt="Two scatter plots of WikiText-2 perplexity against file size. Qwen3-4B: B4 16.64, IQ2_KT best 18.15, IQ2_XXS 28.61, fp16 12.46. Llama-3.1-8B: B4 9.14, IQ2_KT 10.04, IQ2_XXS 12.35." loading="lazy" width="1485" height="467"></a></div>
      <figcaption class="figcap">WikiText-2 perplexity against file size, all at or below llama.cpp's IQ2_XXS size (lower is better). Left: Qwen3-4B, where B4 is the last of many builds (post hoc). Right: Llama-3.1-8B, where the comparison with IQ2_KT was pre-registered before the file was built.</figcaption>
    </figure>

    <div style="height:18px"></div>
    <h3 style="margin:0 0 12px;font-size:18px">How it compares on Qwen3-4B</h3>
    <div class="tbl">
      <table>
        <thead><tr><th>Format</th><th>Size</th><th>Perplexity</th><th>What it is</th></tr></thead>
        <tbody>
          <tr><td class="name">fp16 original</td><td class="num">8.05 GB</td><td class="num"><div class="pbar"><span style="width:43.5%"></span>12.46</div></td><td class="what">Uncompressed reference</td></tr>
          <tr><td class="name">Ternary Bonsai 4B <span class="what">(prism-ml)</span></td><td class="num">1.07 GB</td><td class="num"><div class="pbar"><span style="width:40.5%"></span>11.59</div></td><td class="what">Retrained model; needs a training run and their fork</td></tr>
          <tr><td class="name">GSQ Q2_K <span class="what">(ISTA-DASLab)</span></td><td class="num">1.67 GB</td><td class="num"><div class="pbar"><span style="width:50.2%"></span>14.37</div></td><td class="what">Compression, 34% bigger file</td></tr>
          <tr class="ours"><td class="name">Pingala-8 B4 <span class="tag">ours</span></td><td class="num">1.24 GB</td><td class="num"><div class="pbar"><span style="width:58.2%"></span>16.64</div></td><td class="what">Compression of the original, no retraining</td></tr>
          <tr><td class="name">IQ2_KT <span class="what">(ik_llama.cpp)</span></td><td class="num">1.25 GB</td><td class="num"><div class="pbar"><span style="width:63.4%"></span>18.15</div></td><td class="what">Compression, same size</td></tr>
          <tr><td class="name">IQ2_XXS <span class="what">(llama.cpp)</span></td><td class="num">1.25 GB</td><td class="num"><div class="pbar"><span style="width:100%"></span>28.61</div></td><td class="what">Compression, same size</td></tr>
        </tbody>
      </table>
    </div>
    <p class="note">WikiText-2 test, context 4096, lower is better. <b>If you just want the strongest small Qwen3-4B, a retrained model like Ternary Bonsai scores higher.</b> Pingala-8 is for compressing models nobody has retrained for you: among the formats we tested, it gives the lowest perplexity for the original Qwen3-4B at this size without retraining.</p>

    <div style="height:28px"></div>
    <div class="two">
      <div>
        <h3 style="margin:0 0 12px;font-size:18px">Zero-shot quiz scores, Qwen3-4B</h3>
        <div class="tbl">
          <table style="min-width:540px">
            <thead><tr><th>Test</th><th>8-bit original</th><th>IQ2_XXS</th><th>IQ2_KT</th><th>Ours</th><th>vs IQ2_XXS</th></tr></thead>
            <tbody>
              <tr><td>ARC-Easy</td><td class="num">66.1</td><td class="num">43.9</td><td class="num">53.3</td><td class="num up">60.9</td><td class="num up">+39%</td></tr>
              <tr><td>ARC-Challenge</td><td class="num">45.2</td><td class="num">29.1</td><td class="num">35.1</td><td class="num up">37.8</td><td class="num up">+30%</td></tr>
              <tr><td>HellaSwag</td><td class="num">65.8</td><td class="num">47.8</td><td class="num">53.3</td><td class="num up">57.7</td><td class="num up">+21%</td></tr>
              <tr><td>WinoGrande</td><td class="num">65.5</td><td class="num">57.5</td><td class="num">57.9</td><td class="num up">63.8</td><td class="num up">+11%</td></tr>
              <tr><td>MMLU</td><td class="num">38.7</td><td class="num">31.1</td><td class="num">32.9</td><td class="num up">34.0</td><td class="num up">+9%</td></tr>
              <tr class="ours"><td class="name">Average</td><td class="num">56.3</td><td class="num">41.9</td><td class="num">46.5</td><td class="num up">50.8</td><td class="num up">+21%</td></tr>
            </tbody>
          </table>
        </div>
        <p class="note">All 2-bit files are the same 1.24 GB. Accuracy in %, higher is better.</p>
      </div>
      <div>
        <h3 style="margin:0 0 12px;font-size:18px">Llama-3.1-8B, pre-registered</h3>
        <div class="tbl">
          <table style="min-width:480px">
            <thead><tr><th>Format</th><th>Size</th><th>PPL, BOS</th><th>PPL, strided</th></tr></thead>
            <tbody>
              <tr><td class="name">fp16 original</td><td class="num">16.08 GB</td><td class="num">-</td><td class="num">-</td></tr>
              <tr class="ours"><td class="name">Pingala-8 B4 <span class="tag">ours</span></td><td class="num">2.39 GB</td><td class="num up">8.44</td><td class="num up">9.14</td></tr>
              <tr><td class="name">IQ2_KT</td><td class="num">2.39 GB</td><td class="num">9.36</td><td class="num">10.04</td></tr>
              <tr><td class="name">IQ2_XXS</td><td class="num">2.40 GB</td><td class="num">-</td><td class="num">12.35</td></tr>
            </tbody>
          </table>
        </div>
        <p class="note">WikiText-2, lower is better. "BOS": a BOS token at every window. "Strided": the protocol in the chart above. The win over IQ2_KT was predicted in writing before the file was built.</p>
      </div>
    </div>

    <div class="callout">
      <div class="big">39%</div>
      <p><b>Faster than Q4_K_M at half its size.</b> On one RTX 5070 Ti Laptop GPU, batch-1 generation runs at 1.10x the speed of llama.cpp's IQ2_XXS. Prompt processing is slower (0.74x), so long pasted documents take longer to read in.</p>
    </div>
  </section>

  <!-- LIMITS -->
  <section id="limits">
    <p class="kicker">Honest limits</p>
    <h2>Where we lose</h2>
    <p class="sub">Please read these before you pick a format. We would rather you know them up front.</p>
    <div class="limits">
      <div class="lim"><h3>Qwen3-14B: IQ2_KT wins</h3><p>Perplexity 9.57 for us against 9.40 for IQ2_KT (a tie on C4). We wrote the prediction down before the run, so this is a real loss.</p></div>
      <div class="lim"><h3>A retrained model scores higher</h3><p>Ternary Bonsai 4B reaches 11.59 at 1.07 GB. It is a retrained model, not a compression of Qwen3-4B, and needs a training run plus its own fork.</p></div>
      <div class="lim"><h3>Prompt reading is slower</h3><p>Prompt processing runs at 0.74x the speed of IQ2_XXS, and serving several chats at once is 3 to 5 times slower.</p></div>
      <div class="lim"><h3>Patched llama.cpp, CUDA only</h3><p>Stock llama.cpp, Ollama and LM Studio cannot load these files yet. No Metal or Vulkan path. Speed was measured on one laptop GPU.</p></div>
      <div class="lim"><h3>The Qwen3-4B file is tuned</h3><p>It is the last of many builds evaluated on WikiText-2, so treat that result as post hoc. C4 text, Llama-3.1-8B and Qwen3-14B were held out or pre-registered.</p></div>
      <div class="lim"><h3>2 bits still costs quality</h3><p>The model is clearly worse than full precision: 16.64 against 12.46 perplexity for fp16 Qwen3-4B.</p></div>
    </div>
  </section>

  <!-- TRY -->
  <section id="try">
    <p class="kicker">Try it</p>
    <h2>Chatting in about two minutes</h2>
    <p class="sub">Windows, NVIDIA RTX 30/40/50 with a recent driver. No Python, no build step.</p>
    <div class="steps">
      <div class="step"><div class="k">1</div><h3>Download</h3><p>Get <code>pingala8-windows-cuda12.zip</code> from the <a href="https://github.com/Genrius-AI/pingala8-llm/releases/latest" target="_blank" rel="noopener">latest release</a>.</p></div>
      <div class="step"><div class="k">2</div><h3>Unzip and double-click</h3><p>Run <code>run_qwen3_4b.bat</code> (or <code>run_llama31_8b.bat</code>). The model downloads once from this Hugging Face org.</p></div>
      <div class="step"><div class="k">3</div><h3>Chat</h3><p>A chat page opens at <code>http://127.0.0.1:8080</code> in your browser. Close the black window to stop.</p></div>
    </div>
    <p class="note"><a href="https://huggingface.co/spaces/Genrius-AI/llm-vram-calculator" target="_blank" rel="noopener noreferrer">Will it fit your GPU? Normal vs Pingala-8 calculator</a> &rarr;</p>
    <div style="height:16px"></div>
    <pre><span class="c"># Linux, from the same release</span>
./run.sh qwen      <span class="c"># or: ./run.sh llama</span>
<span class="c"># then open http://127.0.0.1:8080</span>

<span class="c"># Build from source and reproduce the numbers</span>
git clone https://github.com/Genrius-AI/pingala8-llm   <span class="c"># see README.md and REPRODUCE.md</span></pre>
    <div class="why">
      <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><circle cx="12" cy="12" r="9"/><path d="M12 8h.01"/><path d="M11 12h1v4h1"/></svg>
      <span>Why there is no live chat on this page: the format runs on our patched llama.cpp with CUDA, and this free Space has no GPU. The video above is the closest thing, recorded in real time.</span>
    </div>
  </section>

  <!-- MODELS -->
  <section id="models">
    <p class="kicker">Get the files</p>
    <h2>Models, code and paper</h2>
    <p class="sub">GGUF files for the patched llama.cpp runtime. Pass the first shard; llama.cpp finds the others. Everything is also in one <a href="https://huggingface.co/collections/Genrius-AI/pingala-8-2-bit-llms-for-llamacpp-6adcae08d49bbdd8dd446ca9" target="_blank" rel="noopener">Hugging Face collection</a>.</p>
    <div class="cards">
      <a class="card" href="https://huggingface.co/Genrius-AI/Qwen3-4B-Pingala8-2bit" target="_blank" rel="noopener">
        <div class="t"><h3>Qwen3-4B Pingala-8 2-bit</h3><span class="sz">1.24 GB</span></div>
        <p>From 8.05 GB. Perplexity 16.64 vs 28.61 for IQ2_XXS at the same size. Works with the one-click chat build.</p>
        <div class="meta"><span class="chip">Apache-2.0</span><span class="chip">GGUF</span><span class="chip">2-bit</span></div>
        <span class="go">Genrius-AI/Qwen3-4B-Pingala8-2bit &rarr;</span>
      </a>
      <a class="card" href="https://huggingface.co/Genrius-AI/Llama-3.1-8B-Pingala8-2bit" target="_blank" rel="noopener">
        <div class="t"><h3>Llama-3.1-8B Pingala-8 2-bit</h3><span class="sz">2.39 GB</span></div>
        <p>From 16.08 GB. Pre-registered win over IQ2_KT: 8.44 vs 9.36. Base model, best for plain text completion. Built with Llama.</p>
        <div class="meta"><span class="chip">Llama 3.1 Community License</span><span class="chip">GGUF</span><span class="chip">2-bit</span></div>
        <span class="go">Genrius-AI/Llama-3.1-8B-Pingala8-2bit &rarr;</span>
      </a>
    </div>
    <div class="small-cards">
      <a class="card" href="https://github.com/Genrius-AI/pingala8-llm" target="_blank" rel="noopener"><h3>Code and patch</h3><p>CUDA patch for llama.cpp, build scripts and every log behind these numbers.</p><span class="go">GitHub &rarr;</span></a>
      <a class="card" href="https://github.com/Genrius-AI/pingala8-llm/releases/latest" target="_blank" rel="noopener"><h3>One-click build</h3><p>Windows zip with the patched runtime and a browser chat page. Linux script included.</p><span class="go">Release v2.4 &rarr;</span></a>
      <a class="card" href="https://doi.org/10.5281/zenodo.23481757" target="_blank" rel="noopener"><h3>Paper</h3><p>Small-State Coset Trellis Coding at IQ2_XXS Size: An Equal-Bytes Study in llama.cpp.</p><span class="go">DOI 10.5281/zenodo.23481757 &rarr;</span></a>
    </div>
  </section>

  <!-- HOW -->
  <section id="how">
    <p class="kicker">How it works</p>
    <h2>A small trellis code, inside a GGUF file</h2>
    <div class="how">
      <div>
        <p>Pingala-8 stores an <b>8-state trellis code</b> (Ungerboeck's set partitioning) over groups of 4 weights. Before coding, each weight matrix goes through a <b>random Hadamard rotation</b> and <b>QuIP-style LDLQ rounding</b>, and input-column scales are set by minimising an incoherence bound.</p>
        <p>The result is a normal GGUF file at no more bytes than llama.cpp's IQ2_XXS, decoded on the GPU by a CUDA patch to llama.cpp. It builds on QTIP and QuIP# and on ikawrakow's IQ2_KT.</p>
        <p>Compressing a model takes a few hours on one GPU. There is no retraining: the rounding step only needs some calibration text.</p>
      </div>
      <div class="pipe" aria-label="Pipeline">
        <div><b>Original weights</b>fp16 / bf16 checkpoint you already have</div>
        <div class="arr">&darr;</div>
        <div><b>Hadamard rotation + LDLQ rounding</b>spreads outliers, rounds with error feedback</div>
        <div class="arr">&darr;</div>
        <div><b>8-state trellis over 4-weight symbols</b>2-bit class weights</div>
        <div class="arr">&darr;</div>
        <div><b>GGUF + patched llama.cpp (CUDA)</b>same file size as IQ2_XXS</div>
      </div>
    </div>
  </section>

  <!-- CITE -->
  <section id="cite">
    <p class="kicker">Cite</p>
    <h2>If you use Pingala-8</h2>
    <div class="cite">
      <button class="copy" type="button" id="copybtn">Copy</button>
<pre id="bib">@misc{goyal2026pingala8,
  title     = {Small-State Coset Trellis Coding at IQ2_XXS Size:
               An Equal-Bytes Study in llama.cpp},
  author    = {Goyal, Harshit},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.23481757},
  url       = {https://doi.org/10.5281/zenodo.23481757}
}</pre>
    </div>
  </section>

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      <div>
        <p><b style="color:var(--beige);letter-spacing:.18em;text-transform:uppercase;font-size:12px">Genrius AI</b></p>
        <p>Pingala-8 and Genrius AI are names of Genrius AI. Code: Apache-2.0. llama.cpp is MIT.</p>
        <p>Qwen3-4B is Apache-2.0. Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Built with Llama.</p>
      </div>
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        <p><a href="https://huggingface.co/Genrius-AI" target="_blank" rel="noopener">Hugging Face</a> &middot; <a href="https://github.com/Genrius-AI/pingala8-llm" target="_blank" rel="noopener">GitHub</a> &middot; <a href="https://doi.org/10.5281/zenodo.23481757" target="_blank" rel="noopener">Paper</a></p>
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