moco optimizes machine learning classifiers so easy inputs finish early, reducing unnecessary compute while preserving model accuracy.
High-throughput ML systems must provision large GPU fleets to meet strict latency requirements.
But every request typically runs the full model—even when the input is easy to classify.
This wastes significant compute.
Many inputs can be classified correctly long before the final layer of a model.
moco adds early-exit paths to existing classifiers so easy inputs skip the remaining layers.
Easy inputs → exit early
Hard inputs → run the full model
The result: less compute per request and lower infrastructure cost.
Example GPU savings:
moco analyzes a trained classifier and adds a lightweight probe at an intermediate layer that can classify certain inputs without running the rest of the model.
| Task | Model | Latency Reduction | Accuracy Impact |
|---|---|---|---|
| Image Classification | ResNet-18 | 34.6% | -0.3% |
| Text Classification | BERT | 21.5% | +0.1% |
Lower compute requirements mean fewer GPUs needed to serve the same workload.
Handle more requests per second without adding hardware.
Deploy more complex classifiers within existing compute budgets.
Run a 30-day evaluation to measure GPU savings on your inference workload.