quarterbit 0.1.7

Precision optimizer for PyTorch - 1,000,000x more accurate than FP32

QuarterBit

The Pareto-Optimal Optimizer for PyTorch

Better precision. Less memory. Faster training. No tradeoffs.

The Problem

Standard FP32 training loses precision over long runs. Tiny gradient updates get rounded away, causing:

• Stalled convergence in late training
• Wasted GPU hours
• Suboptimal final models

The Solution

QuarterBit's CompactEFTAdam combines compressed storage with EFT (Error-Free Transformation) arithmetic to achieve:

Metric	PyTorch Adam	CompactEFTAdam	Improvement
Precision	Loses 100% of tiny updates	Loses 0%	1,000,000x
Memory	16 B/param	9.25-13.25 B/param	17-42% savings
Convergence	41 steps to target	27 steps	34% faster

Installation

pip install quarterbit

Quick Start

from quarterbit.torch import CompactEFTAdam

# Drop-in replacement for torch.optim.Adam
optimizer = CompactEFTAdam(model.parameters(), lr=1e-3)

# Train as usual
for batch in dataloader:
    loss = model(batch)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

Why QuarterBit?

1. Precision That Matters

After 500K training steps, standard FP32 loses 100% of tiny gradient updates. QuarterBit's EFT arithmetic preserves every bit.

2. Memory Efficiency

Train larger models on the same GPU. CompactEFTAdam uses compressed FP16+FP4 storage, saving 17-42% memory.

3. Faster Convergence

Better precision = faster convergence. Reach your target loss in 34% fewer steps.

4. Drop-In Replacement

No code changes needed. Just swap your optimizer.

Benchmarks

See our Kaggle notebook for full benchmarks on GPT-2.

Requirements

• Python 3.8+
• PyTorch 2.0+
• NVIDIA GPU with CUDA support

Pricing

Tier	Price	Use Case
Free	$0	Personal, research, evaluation (<10 GPU-hrs/mo)
Pro	$299/mo	Commercial use, up to 10 GPUs
Team	$2,499/mo	Up to 100 GPUs, priority support
Enterprise	Custom	Unlimited GPUs, custom SLA

See quarterbit.dev/pricing for details.

License

Proprietary - see LICENSE for details. Free tier available for non-commercial use.

Links

• Website: quarterbit.dev
• GitHub: github.com/DigitalMax321/quarterbit
• Email: [e-mail]