The World's Most Efficient Foundation Models

Elevate Your AI Capabilities with Deci's
Powerful Foundation Models

Power your applications with accurate and highly efficient foundation models generated with Deci’s AutoNAC technology. Gain full control over your models’ quality, data security and inference cost.

Pre-Trained Models for Every Task

Accurate and efficient computer vision models for any task, dataset, and hardware. Including: YOLO-NAS, DeciSeg, DeciNet, DeciDet, among others.

Generate your custom model with AutoNAC.
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Accelerate Your AI Journey with Deci's Efficient Models

Launch your AI-Powered
Applications Faster

Use enterprise-grade models. Lower risk, shorten dev time from months to days.

Cut your Training &
Fine-Tuning Cost

Deci’s NAS generated foundation models are trained and fine-tuned 2x faster compared to other models.

Scale Up Inference
Cost-Effectively

Save up to 80% on your inference cost with fast and low memory footprint models. Migrate workloads to affordable & widely available GPUs.

Improve User Experience
with Faster Inference

Ship better products and delight users with up to 5x lower latency performance compared to other SOTA models.

"At Adobe, we're reshaping digital experiences with top-tier AI solutions. With Deci, we sped up market entry, shifted inference from cloud to edge, enhancing user experience and cutting costs."

Pallav Vyas,
Senior Engineering Manager, Document AI & Innovation

Why Deci's Models?

Unparalleled
Performance

The world's most efficient and cost effective foundation models.

Control Quality
& Customization

Gain competitive edge through advanced model customizations.

Full Data
Privacy

Self-hosted inference. No vendor lock-in. Ideal for enterprises and for handling sensitive data.

Deci's Models are Generated with AutoNAC™ - Cutting Edge NAS Technology.

Add Your Heading Text Here
				
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")