MCG — Modular Compute Governor
MCG is a model intelligence platform that maps the internal structure of neural networks, identifying which layers are critical, which are harmful, and which are redundant. Model optimization in a single analysis run, not a full training cycle. Like an MRI for AI models: first understand, then optimize with proof.
Your model is a black box
Organizations deploy AI models without insight into their internal structure. When models are fine-tuned, critical layers can change without anyone noticing. When models are compressed to save costs, there is no guarantee the compression is safe. And when regulatory requirements tighten, provable documentation of what the model actually contains remains an unsolved problem.
Existing compression tools treat the model as a black box: apply technique, measure output, hope for the best. Which parts are critical and which are redundant is still unknown.
Separate measurement from decision
MCG separates intelligent measurement (AI-driven, adaptive) from decision-making (deterministic, inspectable, reproducible). One is intelligent. The other is deterministic.
Model DNA
A complete structural map of every layer's role. Three stable archetypes emerge consistently across architectures: critical layers that must never be touched, context-dependent layers that need validation, and redundant layers that can be safely removed. Every model has a unique profile, comparable over time.
Harmful layer discovery
MCG does not only find redundant layers. In verified experiments, MCG has identified layers that actively degrade model performance: removing them improves both quality and speed simultaneously. These are layers no compression tool would flag.
Verified layer removal
Redundant or harmful layers are physically removed. No retraining, no labels, no new pipelines. The result is a smaller, faster model in standard format, ready for deployment. Model optimization in a single analysis run instead of a full training cycle.
Direct compute savings
For organizations training or running self-hosted models, removing redundant layers directly reduces compute cost. In cloud training, fewer FLOPs means shorter runs and lower bills. On edge devices, a smaller FLOP budget means models fit on hardware that was previously too constrained. As API pricing moves toward compute-based billing, FLOP reduction translates directly to lower marginal cost.
Stacks with your existing tools
MCG is additive. Layer removal compounds with quantization, LoRA, and FlashAttention. You don't replace your optimization pipeline, you add structural intelligence to it.
Governance Report
Every analysis produces a structured report: executive summary, Model DNA map, archetype distribution, redundancy report, recommended configuration, optimized model, methodology, reproducibility data, and limitations. Everything inspectable. Everything reproducible.
Not better pruning, a different approach
Pruning removes individual weights after training and degrades quality at high compression rates. Distillation requires training a new model from scratch. MCG identifies structural redundancy during training and removes entire layers while keeping all remaining weights at full original precision.
Existing approaches
Remove or simplify weights after training. Quality always degrades at high compression. No structural understanding. No proof of safety. Distillation takes weeks of training.
MCG
Map structural redundancy in a single analysis run. Remove entire layers with full-precision weights intact. Standard output format with no vendor lock-in. The model tells you what it doesn't need, and sometimes what actively hurts it.
From model audit to model governance
Today: Model Audit
Structural analysis, layer classification, verified removal, Governance Report. A complete picture of what's inside your model and what can be safely optimized. Verified across 10+ architectures.
Next: Governance pipeline
Deterministic safety checks at every step of the optimization. Convergence validation, consistency verification across runs, optimal removal point calculation, coherence checks after removal. Fine-tuning drift detection that catches catastrophic forgetting before it reaches production. Deploy gates in CI/CD that block unsafe changes. Fabrication detection integrated as an additional quality signal.
Vision: Model Control Layer
Dynamic per-input layer activation. Cross-product intelligence with TTU Router. Model DNA as the AI equivalent of a software bill of materials. The infrastructure for trusted AI.
MCG — Common questions
How does MCG differ from model pruning?
Pruning removes individual weights and degrades quality at high compression. MCG removes entire layers while keeping all remaining weights at full precision. It also identifies harmful layers that pruning tools would never flag. No retraining required.
Does MCG work with my existing optimization stack?
Yes. MCG is additive and has been verified alongside quantization and LoRA. Layer removal compounds with your existing tools, you don't need to replace anything.