Run Real-Time Inference at the Edge

Improve latency and throughput and reduce model size by up to 10x while maintaining your models’ accuracy.

Enable New Applications on Edge Devices

Improve model inference and reduce model size and memory footprint to run on resource constrained devices without compromising on accuracy.

Scale up Inference on Existing Edge Devices

Make the most of your devices and scale up inference more cost efficiently with better hardware utilization.

Migrate Workloads from
Cloud to Edge

Enables new applications on edge devices with smaller and more efficient models.

Migrate Inference Workloads From Cloud to Edge

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Discover Tips to Accelerate Inference Performance of Your AI Applications

Get Similar Results for Your Specific Use Case

Enabling Real-Time Semantic Segmentation for an ADAS Use Case

Semantic Segmentation

An automotive firm faced challenges hitting their throughput goals with a U-Net model on NVIDIA Jetson Xavier NX. By employing Deci’s AutoNAC engine, they developed a quicker, smaller model, cutting latency by 2.1X, shrinking the model size by 3X, and reducing memory usage by 67%, without sacrificing accuracy.

See How It Works

Watch a quick walkthrough of how you can use Deci to accelerate your models’ inference performance. 

Play Video

“At RingCentral, we strive to provide our customers with the best AI-based experiences. With Deci’s platform, we were able to exceed our deep learning performance goals while shortening our development cycles. Working with Deci allows us to launch superior products faster.”

Vadim Zhuk, Senior Vice President

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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