Video classification
Video becomes frames, and frames can trigger multiple classifiers. At scale, tiny savings per frame compound rapidly.
1 minute × 30 FPS = 1,800 frames
Inference optimization for high-volume decisions
More decisions per GPU-hour.
moco reduces the compute required by 20-50% to make high-volume ML classification decisions. If your infrastructure processes millions or billions of images, video frames, documents, text objects, or events, moco identifies decisions that don't require the full model and routes them through cheaper computation paths.
The economics
Your inference bill scales with every decision.
Modern ML systems increasingly make decisions at enormous scale. A single customer request can trigger multiple model executions. A single video can become thousands of frame-level classifications. A document can become hundreds of page- and token-level decisions. Every classification has a cost, based on how many compute-hours it uses.
Objects
×
Model executions / object
×
Compute / execution
=
Inference compute
When volume is large enough, improvements in compute efficiency become significant infrastructure savings.
How it works
Stop spending the same compute on every decision.
Most classification models process easy and difficult examples through essentially the same computational path. But they don't require the same amount of computation.
Traditional inference
Input → Full Model → Decision
Every input gets the same computation.
Input → Full Model → Decision
→
moco
Input → Cheap Decision → Easy? → Stop
↘ Difficult → Full Model → Decision
Easy decisions terminate early. Difficult decisions receive deeper computation.
Input → Cheap Decision → Easy? → Stop
↘ Difficult → Full Model → Decision
moco finds sufficient conditions for making easy decisions earlier, then constructs computational paths that avoid unnecessary full-model execution.
The KPI
We optimize for decision capacity.
Primary metric
Decisions per GPU-hour
Increasing decisions per GPU-hour can mean fewer GPU-hours for the same workload, higher throughput on existing hardware, fewer servers, lower inference COGS, and more customer volume without proportional infrastructure growth.
The objective is more useful decisions per unit of compute.
Workloads
Built for workloads where volume creates the cost.
moco is most valuable when inference volume is enormous and compute is a material operating expense.
Image classification
Large-scale image APIs classify enormous volumes for moderation, visual search, tagging, safety, identity, and quality control.
More images → more inference → more compute
Document processing
Documents can generate decisions at the document, page, region, and text levels for routing, compliance, fraud, and extraction.
More pages → more decisions
Cybersecurity
Classify URLs, files, emails, identities, network events, and user actions across large asynchronous security pipelines.
High event volume + multiple models
Fraud & financial decisions
High-volume transaction and account systems make repeated classification decisions where inference cost can compound.
Transactions × models × compute
Industrial inspection
Manufacturing and physical-economy systems can classify every item, component, image, or sensor observation moving through a process.
Every object can require a disposition
Who it's for
For companies operating classification infrastructure at scale.
High volume
Millions to trillions of objects or events processed annually.
Asynchronous inference
Throughput and compute efficiency matter more than single-request latency.
Multiple model executions
One object may trigger multiple classifiers or model evaluations.
Material inference COGS
Inference compute is large enough to be a meaningful infrastructure expense.
Continuous workloads
Infrastructure operates continuously or at high utilization.
Throughput is valuable
Increasing decisions per GPU-hour creates measurable economic or operational value.
Inference compute economics
What does $1 million of inference compute actually buy?
Inference cost ultimately comes down to compute-hours. Every classification consumes some amount of GPU capacity. At high enough volume, even a fraction of a cent per decision becomes a significant infrastructure expense.
Objects processed
×
Model executions / object
×
Compute / execution
=
GPU-hours required
Consider a production inference workload that consumes approximately $1M of GPU compute per year. Using an H100 at roughly $5.19 per GPU-hour as a reference, that represents approximately 193,000 H100 GPU-hours.
$5.19
approximate hourly cost of one H100 GPU
193K
H100 GPU-hours required for $1M of compute
22
H100 GPUs running continuously for one year
The same economics apply whether the model is making a binary classification, ranking an object, detecting fraud, determining whether an image contains prohibited content, or predicting the next token with deterministic decoding. The underlying question is the same: how much computation is required to produce the decision?
A $1M inference workload
$1,000,000 → $800,000
Suppose a production system processes billions of decisions per year and requires $1M of GPU compute to execute its current inference pipeline. If moco eliminates 20% of the required computation, the same workload requires approximately $800K of GPU compute.
$1,000,000 → $800,000
$200K/year of compute capacity recovered.
There is another way to view the same improvement. If the original workload requires 100 GPU-hours per 100 decisions, and optimization reduces that requirement to 80 GPU-hours, the system can process approximately 25% more decisions on the same hardware. That is the economic meaning of more decisions per GPU-hour.
Inference cost
=
GPU-hours
×
GPU-hour price
Public cloud GPU pricing is only a reference point. Real production economics also include utilization, reserved capacity, networking, storage, orchestration, idle capacity, and the hardware configuration required to serve the model. The important variable for moco is the amount of compute capacity consumed by the workload.
Technical
From one model to a decision system.
moco operates as a post-training optimization layer. It analyzes model behavior, data, intermediate computations, and predictions to identify regions of the input space where additional computation is unnecessary.
Analyze
Find structure in model predictions and intermediate representations.
Discover
Identify sufficient conditions for making easy decisions earlier.
Route
Construct cheaper computational paths for easy inputs.
Fallback
Send difficult inputs through the original deeper computation.
Designed for classification architectures including transformers, CNNs / ResNets, MLPs, tree-based models, and other production ML systems.
Free assessment
Find out whether your workload has compute waste.
Send us representative inference data, model outputs, or inference logs. We'll analyze the workload to determine whether cheaper computational paths are feasible.
- Potential reduction in GPU-hours
- Potential increase in decisions per GPU-hour
- Potential throughput improvement
- Whether early-exit or cascaded computation is feasible
- Estimated infrastructure savings
The question
How much compute does the world spend making decisions that don't require all of that computation?
moco is building infrastructure to make those decisions cheaper.