Team

Cut GPU inference costs by 20–35%

moco optimizes machine learning classifiers so easy inputs finish early, reducing unnecessary compute while preserving model accuracy.

~30% fewer FLOPs ~30% fewer GPUs ~30% higher throughput ~30% lower latency

Pilot Program

Run a 30-day evaluation to measure GPU savings on your inference workload.

Book an evaluation

The Problem

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.

The Idea

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.

The Solution

Example GPU savings

Typical Inference Cluster
$2.6M / year 100 GPUs
With moco Optimization
$1.8M / year 70 GPUs
↓ 30% GPU usage • ↓ $800K/year

How It Works

As a post-training optimization step, moco analyzes a calibration dataset (e.g training), considers a specific representation (either the dataset itself or a representation pulled from an intermediate layer) and determines regions of the dataset where only one class is present. If this rule activates, it means the rule is confident in its decision, and can avoid execution of the rest of the model.

Input

  • Pre-trained model
  • Calibration dataset

Output

  • Optimized model
  • Lower compute per request

Integration

  • Standard PyTorch model artifact
  • Compatible with existing inference pipelines

Benchmarked Results

Task Dataset Base Model Fine Tuned Model Compute Reduction Accuracy Change
Image Classification CIFAR-10 ResNet-18 HF Model finetuned on CIFAR-10 34.6% -0.3%
Text Classification IMDB Reviews BERT Fine-Tuned TinyBERT 21.5% +0.1%

Use Cases by Industry

Cybersecurity

High-volume threat detection systems (phishing, malware, intrusion detection) must scan massive streams of events under strict latency constraints.

  • Problem: Every event runs full model inference, even low-risk traffic
  • With moco: Early-exit on high-confidence benign or malicious patterns
  • Impact: Increased coverage under fixed compute budget
  • Outcome: More threats evaluated per second, lower infra cost

Fraud Detection

Payment systems and identity verification pipelines require real-time decisions while minimizing false positives and customer friction.

  • Problem: Expensive models run on every transaction
  • With moco: Fast-path classification for low-risk transactions
  • Impact: Lower latency at peak traffic
  • Outcome: Reduced compute cost + faster approvals

Autonomous Vehicles

Perception systems must process sensor data under strict real-time constraints with high reliability.

  • Problem: Full model inference on every frame increases latency
  • With moco: Early exit on easy frames (clear roads, static scenes)
  • Impact: Reduced compute per frame
  • Outcome: Lower energy consumption → longer battery life

Reduce Infrastructure Cost

Lower compute requirements mean fewer GPUs needed to serve the same workload.

Increase Throughput

Handle more requests per second without adding hardware.

Run More Complex Models

Deploy more complex classifiers within existing compute budgets, reducing false-negatives and false-positives.

Pilot Program

Run a 30-day evaluation to measure GPU savings on your inference workload.

Book an evaluation