I built QuarterBit because AI training costs are insane. A 70B model needs 840GB of memory — that's 11 A100 GPUs at $30+/hour.

QuarterBit AXIOM compresses training memory 15x. Same model. Same quality. Fraction of the hardware.

RESULTS:

  Llama 70B: 840GB → 53GB (11 GPUs → 1 GPU) = 90% savings
  Llama 13B: 156GB → 9GB (FREE on Kaggle T4) = 100% savings

91% energy reduction vs standard training. 100% trainable weights (not LoRA/adapters). 3 lines of code.

HOW IT WORKS:

  from quarterbit import axiom
  model = axiom(model)
  model.cuda()

TRY IT:

  pip install quarterbit

Demo (FREE): https://www.kaggle.com/code/kyleclouthier/quarterbit-axiom-1...

Benchmarks: https://quarterbit.dev

AXIOM uses a novel weight representation combining lossless compression with a built-in optimizer. Weights stored at 0.62 bytes/param vs 4 bytes FP32. Gradient updates happen directly in compressed space.

Not quantization-aware training or LoRA — every parameter fully trainable, convergence matches AdamW.

Solo founder from Canada. Self-taught CUDA/ML. Applying to YC S26.

Happy to answer questions.

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