Samsung open-sources LittleBit, squeezing a 13B LLM under 1GB with 11.6x inference speedup
ChrSzegedy · x · 2026-10-08
Samsung has open-sourced LittleBit, an extreme compression method that shrinks a 13B-parameter LLM to under 1GB.
How it works:
- Instead of storing weights as standard numbers, it uses latent factorization to push weights down to sub-1-bit levels — as low as 0.1 bits per weight in some configurations.
- At this compression level, it replaces heavy floating-point matrix multiplication with a simple bitwise XOR operation — sign flips instead of math.
Results:
- Up to 11.6x inference speedup vs. standard FP16 models.
- Radically reduced memory footprint and loading bandwidth.
- Stays robust in the extreme sub-0.5-bit regime where prior compression methods fail catastrophically.
The approach could reshape model deployment, making large models runnable on low-end hardware.
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