quarterbit 1.0.0

AXIOM - High-performance quantized optimizer with 73% memory savings

QuarterBit AXIOM

High-Performance Quantized Optimizer

73% memory savings. Drift-free precision. Beats AdamW convergence.

The Problem

Training large AI models requires massive GPU memory for optimizer states. AdamW stores 8 bytes per parameter just for momentum and variance — that's 8GB for a 1B parameter model.

Additionally, long training runs suffer from floating-point drift — tiny gradient updates accumulate rounding errors over millions of steps, causing:

• Stalled convergence in late training
• Numerical instability
• Suboptimal final models

The Solution

QuarterBit AXIOM solves both problems:

Metric	AdamW	AXIOM	Improvement
Memory per param	8.0 bytes	2.14 bytes	73% savings
Precision drift	Accumulates errors	Drift-free	Eliminated
Convergence	Baseline	3-4% better	Faster training

Installation

pip install quarterbit

Supported GPUs:

• NVIDIA T4, V100, A100, L4, L40
• NVIDIA RTX 30 series (3060-3090)
• NVIDIA RTX 40 series (4060-4090)
• NVIDIA H100, H200

Quick Start

from quarterbit import Axiom

# Drop-in replacement for AdamW
optimizer = Axiom(
    model.parameters(),
    lr=5e-4,
    weight_decay=0.1,
    total_steps=10000
)

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

Why AXIOM?

1. 73% Memory Savings

Train larger models on the same GPU. AXIOM uses FP4 quantization with per-parameter Log8 variance to reduce optimizer state from 8 bytes to 2.14 bytes per parameter.

2. Drift-Free Training

Error-Free Transformations eliminate floating-point accumulation errors. Your model trains with perfect numerical stability from step 1 to step 1,000,000+.

3. Better Convergence

Two-Step Nesterov momentum, temporal coherence, and soft cautious masking deliver 3-4% better validation loss than AdamW.

4. Drop-In Replacement

Same API as PyTorch optimizers. Change one line of code.

Benchmarks

Validated on GPT-2 Small (124M parameters), WikiText-2, 500 steps:

Optimizer	Val Loss	Memory/Param	Savings
AdamW	4.89	8.0 B	baseline
AXIOM	4.72	2.14 B	73%

AXIOM consistently beats AdamW by 3-4% on validation loss across architectures.

See our benchmark results for full results including multi-model comparisons.

Requirements

• Python 3.8+
• PyTorch 1.8+
• NVIDIA GPU with CUDA support
• Linux or Windows

Pricing

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

See quarterbit.dev/pricing for details.

License

Proprietary - see LICENSE for details. Free tier available.

Links

• Website: quarterbit.dev
• Documentation: quarterbit.dev/docs
• Email: [e-mail]

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