AXIOM Benchmarks

Verified results across model sizes. Train 70B on enterprise GPU or 13B completely FREE on Kaggle.

ENTERPRISE

Llama-2 70B

Blackwell GPU (102GB)

15.7x

Compression

840→53 GB

Memory

FREE

Llama-2 13B

Kaggle T4 (16GB) - $0 cost

17.3x

Compression

156→9 GB

Memory

VERIFIED: Llama-2 70B on Blackwell (Mar 2026)

15.7x

Memory Compression

70B

Parameters Trained

91%

Energy Saved

90%

Cost Savings

Llama-2 70B — Previously Needed 11 GPUs

Now trains on a single GPU with AXIOM

840 GB

Standard Training

11× H100 required

→

53 GB

AXIOM Training

1× GPU

15.7×

Compression

Benchmark Results (Click to Expand)

Click to expand

Memory Breakdown: 840GB → 53GB

Click to expand

Training Dashboard: Loss, PPL, Memory

Click to expand

Energy Crisis: Global Datacenter Projections

Proof of Full Training

Weight Changes (Verified)

LayerNorm weightsδ = 0.0173%

Attention weightsδ = 0.0470%

All layer types show weight changes = real learning, not frozen model

Generation Changes: 5/5

The most significant breakthrough in AI...

when the machines start to learn. That's what I sa...

the ability to explain the "why" behind its decisi...

To build a successful startup...

to have a great idea and strong team. You also nee...

A team that is passionate about what they do and w...

The future of renewable energy...

the sun. The world's first solar-powered airport i...

the development of a new generation of storage sys...

Training Convergence

Perplexity Improvement

522,950

Baseline PPL

→

1.97

Final PPL

265,457×

Improvement

13.17

Baseline Loss

0.68

Final Loss

500

Training Steps

44-63

Tokens/sec

Step-by-Step Training Progression

Step	Train Loss	Val Loss	Perplexity	Memory (GB)	Tok/s
50	9.11	7.75	2320.4	57.6	63
100	5.73	4.63	102.7	57.6	51
200	3.03	1.67	5.3	57.6	47
300	1.54	0.43	1.5	57.6	45
400	0.62	0.39	1.5	57.6	45
500	0.34	0.38	1.5	57.6	44

GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition (102 GB) — Peak memory: 57.6 GB

The Global AI Energy Crisis

+165%

Datacenter power by 2030

Source: Goldman Sachs

2×

Global demand doubling

Source: S&P Global

40+ GW

Power connection backlog

Source: IEA

"Power is #1"

Biggest constraint

— Satya Nadella

If AXIOM Were Widely Adopted by 2030:

222 TWh

Energy saved per year

22M

Homes' worth of electricity

89M

Tonnes CO₂ reduced

For Big Tech: The Competitive Advantage

Current Reality

• GPUs sitting IDLE due to power constraints
• GPU waitlists: 36-52 weeks even with unlimited budget
• Each frontier training run: 20-25 MW for 3 months
• Datacenter build time: 18+ months

With AXIOM

Train 11× more models (same power budget)
Eliminate GPU waitlists (1 GPU vs 11)
Use stranded/idle GPU assets
121× more training runs possible

Standard (70B model)

11× H100 | 7.7 kW | ~5 runs/year

AXIOM (70B model)

1× H100 | 0.7 kW | ~85 runs/year

Democratizing AI: Who Can Train What

Hardware	VRAM	Standard	AXIOM
Gaming Laptop (RTX 4070)	8 GB	0.6B	9B
Gaming Desktop (RTX 4090)	24 GB	1.8B	26B
Workstation (RTX 6000 Ada)	48 GB	3.6B	53B
Cloud (A100 80GB)	80 GB	6B	88B
Cloud (H100 80GB)	80 GB	6B	88B
Blackwell	102 GB	7.6B	112B
B200	192 GB	14.4B	211B

Students & Academia

Train LLaMA-7B on a desktop. PhD research no longer limited by compute.

Startups

Train 70B+ on single cloud GPU. Monthly cost: ~$2,500 vs $500,000+.

Developing Nations

No datacenter infrastructure required. Local language models become feasible.

Enterprise

Train proprietary models on-premise. No cloud dependency for sensitive data.

Memory Efficiency (Bytes per Parameter)

Component	Standard	AXIOM	Compression
Weights	2B	0.75B	2.7x
Optimizer	8B	0B	∞
Gradients	2B	0.06B	33x
Total	12B	0.81B	14.8x

Quick Start

# Install

pip install quarterbit

# Use with any model

from quarterbit import axiom

model = axiom(model) # 15.7x compression enabled

# Train normally

loss.backward(); optimizer.step() # Works with AdamW

Run It Yourself

70B Benchmark (Enterprise)

Requires Blackwell or similar high-end GPU

Notebook Results JSON

13B Benchmark (FREE)

Run on FREE Kaggle T4 - no cost!

Notebook Run on Kaggle

Ready to train larger models?

15.7x memory compression. 91% energy savings. 3 lines of code.

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