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| <div class="post-meta">// 2026-05-13 | Research</div> | |
| <h2>1-Bit Quantization:<br>Shrinking Models to the Bone</h2> | |
| </div> | |
| <div class="post-content"> | |
| <p>What if each weight in a neural network could only be <strong>−1, 0, or +1</strong>? That is the premise of 1-bit quantization, and it is more powerful than it sounds. This post breaks down how it works, why it matters, and where it falls short.</p> | |
| <h2>What is Quantization?</h2> | |
| <p>A standard neural network stores weights as 32-bit or 16-bit floating point numbers. Those floats carry a lot of information, but also a lot of memory cost. <strong>Quantization</strong> is the process of reducing the precision of those numbers to save space and speed up computation.</p> | |
| <p>Most production models today use <strong>8-bit (INT8)</strong> or <strong>4-bit (INT4)</strong> quantization. These methods compress weights into integers while still preserving enough numeric range to keep quality high. 1-bit takes this to the extreme: <strong>every single weight is represented by just one bit.</strong></p> | |
| <div class="callout"> | |
| <span>// memory comparison for a 7B model</span> | |
| FP16 → ~14 GB<br> | |
| INT8 → ~7 GB<br> | |
| INT4 → ~3.5 GB<br> | |
| 1-bit → ~0.9 GB | |
| </div> | |
| <h2>How Does 1-Bit Actually Work?</h2> | |
| <p>Pure binary quantization maps every weight to either <code>+1</code> or <code>−1</code>. The model learns <em>which sign</em> each weight should carry, not its magnitude. During inference, all multiplications become cheap additions and subtractions, no floating point needed.</p> | |
| <p>The most important recent work in this space is <strong>BitNet</strong> (Microsoft Research, 2023) and its successor <strong>BitNet b1.58</strong> (2024). BitNet b1.58 uses a ternary scheme: weights are constrained to <code>{−1, 0, +1}</code>. The extra zero value turns many operations into a complete no-op, making inference even faster.</p> | |
| <div class="callout"> | |
| <span>// bitnet b1.58 weight constraint</span> | |
| W ∈ {−1, 0, +1} — ternary, not strictly binary<br> | |
| activations are still quantized to INT8 | |
| </div> | |
| <p>It's like a dream for weak hardware users.</p> | |
| <h2>Training vs Post-Training Quantization</h2> | |
| <p>There are two fundamentally different approaches here, and the distinction matters a lot.</p> | |
| <ul> | |
| <li><strong>Post-Training Quantization (PTQ)</strong>: take a pre-trained FP16 model and quantize it after the fact. Fast and convenient, but quality degrades — especially below 4 bits.</li> | |
| <li><strong>Quantization-Aware Training (QAT)</strong>: train the model from scratch with quantized weights. The model adapts to its constraints during training. This is how BitNet works — and it is what makes 1-bit viable at all.</li> | |
| </ul> | |
| <p>Trying to PTQ a standard model down to 1-bit produces catastrophic quality loss. <strong>1-bit only works if the model is trained to be 1-bit from day one.</strong></p> | |
| <h2>The Numbers: How Much Do You Lose?</h2> | |
| <p>The honest answer: <strong>it depends heavily on model size.</strong> Small models suffer more than large ones. A 125M parameter BitNet model loses noticeably more quality than a 7B BitNet model when compared to their FP16 equivalents.</p> | |
| <div class="table-wrap"> | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>Format</th> | |
| <th>Bits/weight</th> | |
| <th>Memory (7B)</th> | |
| <th>Speed</th> | |
| <th>Quality loss</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr><td>FP16</td><td>16</td><td>~14 GB</td><td>baseline</td><td>none</td></tr> | |
| <tr><td>INT8</td><td>8</td><td>~7 GB</td><td>1.5–2×</td><td>minimal</td></tr> | |
| <tr><td>INT4</td><td>4</td><td>~3.5 GB</td><td>2–4×</td><td>low</td></tr> | |
| <tr class="highlight"><td>1.58-bit</td><td>~1.58</td><td>~0.9 GB</td><td>up to 8×</td><td>moderate*</td></tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <p style="font-size:0.85rem; color: var(--muted); margin-top: -1rem;">* at large scale (7B+), quality loss becomes very competitive with INT4.</p> | |
| <h2>Why This Matters for Edge and Tiny Models</h2> | |
| <p>For us at SupraLabs, 1-bit quantization is an interesting reference point. At sub-1M parameters, the scale of Supra Mini, the quality penalty of 1-bit QAT is severe. The model simply does not have enough capacity to absorb the constraint. <strong>At our scale, every bit of precision counts.</strong></p> | |
| <p>Where 1-bit shines is on large models deployed at the edge: think 7B+ models running on phones, embedded devices, or microcontrollers without a GPU. The memory savings are dramatic and the inference speedup from replacing multiplications with additions is real and measurable.</p> | |
| <h2>The Catch</h2> | |
| <p>1-bit is not a free lunch. The main trade-offs are:</p> | |
| <ul> | |
| <li><strong>Requires purpose-built training</strong> = no PTQ shortcut.</li> | |
| <li><strong>It's a 50/50 chance for small models</strong> = it can help our model or kill it lol</li> | |
| <li><strong>Small models suffer</strong> = below ~1B parameters, the quality loss is hard to justify.</li> | |
| <li><strong>Activations still need INT8</strong> = it's not fully binary end-to-end yet.</li> | |
| </ul> | |
| <h2>How this helps us?(and YOU!)</h2> | |
| <p>We, SupraLabs, are going to try every type of experiment, quantization, pruning, distillation, all to create the best models for you!</p> | |
| <h2>Final Thought</h2> | |
| <p>1Bit quantization is a little bit sensitive area for small models, but we are going to try everything to do it works!</p> | |
| <div class="tags"> | |
| <span class="tag">#quantization</span> | |
| <span class="tag">#1bit</span> | |
| <span class="tag">#bitnet</span> | |
| <span class="tag">#tinyml</span> | |
| <span class="tag">#research</span> | |
| <span class="tag">#edge-ai</span> | |
| <span class="tag">#open-source</span> | |
| </div> | |
| </div> | |
| </article> | |
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