Verified: 70B on 1 GPU, 13B for FREE

Why AXIOM?

Train 70B models on a single GPU. Train 13B models completely FREE. 15-17x memory compression. No quality loss.

ENTERPRISE

Llama-2 70B

Standard840 GB (11 GPUs)
AXIOM53 GB (1 GPU)
Compression15.7x
FREE

Llama-2 13B

Standard156 GB ($1,500+)
AXIOM9 GB (FREE Kaggle)
Compression17.3x

The Problem

12 bytes
Per parameter (FP16 + AdamW)
Weights + Optimizer + Gradients
840 GB
To train 70B model
Needs 11 H100 GPUs
$$$
Millions in compute
Only Big Tech can afford

The AXIOM Solution

ComponentStandardAXIOMSavings
Weights2 B/param0.75 B/param2.7x
Optimizer (m+v)8 B/param0 B/param
Gradients2 B/param0.13 B/param15x
Total12 B/param0.88 B/param13.6x

AXIOM eliminates optimizer state entirely and compresses gradients 15x.

3 Lines of Code

pip install quarterbit
from quarterbit import axiom
model = axiom(model) # 15-17x compression
# optimizer.step() for normalization layers

Drop-in replacement. Works with any PyTorch model.

What You Can Train

HardwareStandardWith AXIOM
Gaming Laptop (8GB)0.6B9B
Gaming Desktop (24GB)1.8B26B
Cloud A100 (80GB)6B88B
Blackwell (102GB)7.6B112B

Who Benefits

Students

Train 13B models for free on Kaggle. PhD research without compute grants.

Startups

Train 70B on single GPU. Compete with Big Tech on startup budgets.

Emerging Markets

No datacenter needed. Train local language models anywhere.

Enterprise

Train on-premise. Data never leaves your infrastructure.

Environmental Impact

91%
Energy Saved
Per training run
222 TWh
Annual Savings
If widely adopted by 2030
89M
Tonnes CO2
Emissions prevented

Without AXIOM

  • 70B training needs 11 GPUs ($200K+)
  • 13B training costs $1,500+ in cloud
  • Students can't train real LLMs
  • Only Big Tech can compete

With AXIOM

  • 70B on single GPU (1 Blackwell)
  • 13B completely FREE (Kaggle T4)
  • Anyone can train frontier models
  • AI democratized globally

Ready to Train Bigger?

15-17x memory compression. 91% energy savings. 3 lines of code.