Run Real-Time Inference at the Edge

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

Deep learning can be challenging under the best of circumstances, but deep learning
on edge devices carries extra complexity because of the constrained environment.
Edge hardware devices simply don’t have the flexibility you get in cloud machines
when it comes to OS, drivers, compute resources, memory, testing, and tuning. Failing
to adapt to this environment can lead to delays in deployment. Hence, it’s important
to be aware of the specific requirements as well as potential challenges that can


Average reduction in Inference Cloud Cost


Boost in Model Accuracy


Average Throughput Acceleration


Average reduction in Model Size

Enable New Applications on Edge Devices

Improve model inference and reduce model size and memory footprint to run on resource constrained devices (e.g. mobile, laptops, cameras, etc.) 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 Inference Workloads From Cloud to Edge

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“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