Hy3 1-bit quant shrinks to 89GB with surprising coherence retention
1-bit quantization of Hy3 drops to 89GB while still generating flight simulators and SVGs.
The open-source AI community continues to push the boundaries of model compression. A Reddit user (u/Ok_Technology_5962) posted about a new 1-bit quantization of Hy3, available on Hugging Face as AngelSlim/Hy3-GGUF. Using the IQ1M quantization method, the model is compressed to a mere 89 GB at the smallest end (93 GB typical), a dramatic reduction from its full-precision size. The user, self-described as not affiliated, tested the quantized model for coherence across complex generative tasks.
The results are surprising: the 1-bit Hy3 successfully created a “beautiful, relaxing flight simulator” in a single HTML file with mountains, clouds, and endless procedural terrain, as well as generating intricate SVGs (panda at a picnic, capybara in an onsen, pelican on a bike). This demonstrates that even at extreme 1-bit quantization, large language models can retain remarkable reasoning and creative capabilities. The user notes that as models grow larger, understanding how the smallest quants behave is critical for practical deployment on consumer-grade hardware.
- Hy3 is now available as a 1-bit IQ1M GGUF quant on Hugging Face at 89-93 GB, a massive compression from full precision.
- Despite extreme quantization, the model generated a complete flight simulator HTML and multiple complex SVGs.
- The user tested this to evaluate coherence at the smallest sizes as models continue scaling up.
Why It Matters
1-bit quantization could make huge open-source models runnable on single GPUs, democratizing access to frontier AI capabilities.