Scale Up Inference on Your Existing Data Center Infrastructure

Optimize your deep learning models to maximize the utilization of any hardware, enabling efficient inference without compromising on accuracy.

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The Challenge

The ever growing compute demands of deep learning training and inference is forcing most enterprises to either update existing data centers or build new ones. This workload increase is driving the demand for hardware and dramatically increases data center total-cost-of-ownership (TCO) and cuts product profitability.

Maximize the ROI of Your Hardware with Efficient Models.​


Average Throughput Acceleration


Average Reduction in Model Size


Average Reduction Model Memory Footprint

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Scale up Inference on existing infrastructure without extra cost​

Improve your deep learning models' inference efficiency and run multiple models on the same hardware.

Enable new deep learning use cases on CPUs

Boost model's performance and enable new deep learning applications on your existing CPUs.

Boost Performance
Ship Better Products

Deploy hardware aware models that are designed to make the most out of your specific data center HW to deliver solutions that win.

"We are excited to be working with Deci's platform - it provided amazing results and achieved 4.6x acceleration on a model we ran in production and helped us provide faster service to our customers.”

Daniel Shichman, CEO
WSC Sports Technologies

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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