AION MODELS

Compressed intelligence.Frontier-grade quality.

AION quantizes and optimizes open-source models with targeted fine-tuning: compact models that match frontier performance and quality, running entirely on-premise.

AION Pipeline

From open-source model to enterprise engine

We don't start from scratch: we build on the best open-source models and transform them with a proprietary optimization pipeline.

Selective fine-tuning, advanced quantization and real benchmark validation: every AION model is designed to maximize quality and efficiency on dedicated hardware, without cloud dependency.

Open-source base

Proven architectures (Llama, Mistral, Qwen and more) as a reliable, transparent foundation.

Targeted fine-tuning

Adaptation for domain, tone and specific tasks — only where needed, without retraining the entire model.

AION quantization

69-70% model weight compression with an estimated 1-3% loss vs. the original.

On-premise deploy

Models ready for PGX and local infrastructure, natively integrated with AION Agent.

Quantization

69-70% smaller. Near-zero loss.

The AION quantization pipeline dramatically reduces footprint and latency while maintaining — and in some benchmarks exceeding — original model quality.

On 7B to 70B parameter models, average compression stays between 69% and 70%. Estimated loss rate ranges from 1% to 3%: in practice, the optimized model retains 97-99% of measured capabilities with huge operational gains on dedicated hardware.

69-70%

Average compression

1-3%

Estimated loss rate

97-99%

Quality retained

Indicative values on 7B–70B open-source models in the AION pipeline. 1-3% loss rate on internal benchmarks.

Comparison

Performance that competes with frontier

Compact models, frontier-grade results: on standard benchmarks AION optimized often beats the original open-source base and approaches — or exceeds in specific cases — much larger models.

Scores normalized 0-100 on public benchmarks. AION results include fine-tuning + quantization. Representative values, not a guarantee for every deployment.

+2-3 pt

Beyond original

On several benchmarks the AION model beats the open-source base thanks to targeted fine-tuning and optimization.

~70%

Fewer resources

Same GPUs, more throughput: quantization enables smaller models with comparable quality.

On-premise

Zero egress

No data to external APIs: frontier-grade quality without giving up data control.

Approach

The right strategy for every goal

Not everything requires massive training. AION combines the right techniques at the right time: RAG for knowledge, fine-tuning for alignment, quantization for efficiency.

No training from scratch

Training an LLM from scratch costs millions and requires dedicated GPU clusters for months. For most companies it's neither practical nor economically viable.