Unsloth compresses Kimi K3 from 1.56TB to 594GB with 1-bit quantization
78.9% accuracy retained while cutting model size by nearly 3x — a breakthrough for local AI deployment.
Deep Dive
The model was quantized to 8, 4, 2, and 1 bit. Q8: 8-bit, 1.56 TB, lossless. Q4: 4-bit, 1.51 TB. Q2: 2-bit, 861 GB. Q1: 1-bit, 594 GB. The smallest Q1 model keeps 78.9% accuracy while being almost 3 times smaller than the original. Instructions for running the model are in the model's card submitted by /u/BankApprehensive7612.
Key Points
- Kimi K3 compressed from 1.56TB to 594GB using 1-bit quantization (Q1), a 2.63x reduction.
- Q1 variant retains 78.9% of original model accuracy, with Q8 being lossless at same size.
- Model card includes run instructions; release by Unsloth enables local deployment on consumer hardware.
Why It Matters
Enables massive 1.5TB-class models to run locally, democratizing advanced AI for individuals and small teams.