QuarterBit – Train 70B LLMs on a single GPU

(quarterbit.dev)

3 points | by quarterbit 11 hours ago ago

3 comments

quarterbit 11 hours ago ago
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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