Enterprise model compression
GeoRefine
Lower inference cost without guessing what the model can afford to lose.
Estimated GeoRefine inference savings
$15,000
~ 60% lower inference cost - $10,000/mo remaining
GeoRefine compresses transformer models by reading their geometry before cutting them down. It gives operators a measured path from compression target to inspection, audit, artifact packing, deployment gates, and rollback.
Evidence and coverage
- Qwen result: 50% compression with under 2% perplexity loss.
- Aggressive Qwen result: 90%+ compression with under 5% perplexity loss.
- GPT-2-medium benchmark: 25% attention-head pruning with under 1% perplexity degradation.
- M2 near-lossless pipeline: quality certificates, lossless audit, artifact packing, inspection, and deployment gates.
- Text LLM, vision, video, MoE, MLA/GQA, and VLM compression surfaces.
Enterprise surfaces
Cost Compression
Reduce GPU hours while preserving the target quality budget for the model and workload under review.
Quality Certificates
Attach metrics, lineage, fingerprints, compression decisions, and audit records to each generated artifact.
Model Coverage
Scope text, vision, video, and multimodal transformer families, including MoE, MLA/GQA, and VLM surfaces.
Operator Workflow
Compress, inspect, audit, pack, deploy, and roll back through explicit gates instead of one-off scripts.
Ideal for
- Inference platforms with rising GPU spend.
- Model teams deciding what can be removed without breaking target quality.
- Operators that need auditable artifacts before serving compressed models.
The Qwen figures are measured GeoRefine results, not a universal guarantee. Compression ratio, quality loss, and savings depend on model family, data, serving stack, hardware, and acceptance budget.
* Illustrative estimate. Actual savings depend on model family, workload, serving stack, hardware, and quality budget.