Reach New Performance Heights with Deci

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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 R&D, RingCentral

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Why Deci?

Gain Unparalleled
Performance in No Time

Achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware.

Shorten Time to
Market

Reach production faster with automated tools. No more endless iterations and dozens of different libraries.

Scale Up
Efficiently

Maximize hardware utilization to enable new use cases on resource constrained devices or cut up to 80% of your cloud compute costs.

“Using Deci, we swiftly developed a model that enabled us to expand our offering and further scale our solution on existing CPU infrastructure with significant cost-efficiency.”

Zvika Ashani
Zvika Ashani, CTO at Irisity

“Controlling our inference cloud spend without compromising on performance is key for our business success. Deci enabled us to scale our workloads while reducing costs and improving our users’ experience.”

Dr. Yair Adato
Founder & CEO at BRIA

“At Adobe, we deliver excellent AI-based solutions across a wide range of cloud and edge environments. By using Deci, we significantly shortened our time to market and transitioned inference workloads from cloud to edge devices. As a result we improved the user experience and dramatically reduced our spend on cloud inference cost.”

Pallav Vyas
Senior Engineering Manager, Document AI & Innovation at Adobe

“Our advanced text to videos solution is powered by proprietary and complex generative AI algorithms. Deci allows us to reduce our cloud computing cost and improve our user experience with faster time to video by accelerating our models’ inference performance and maximizing GPU utilization on the cloud.”

Lior Hakim
Co-Founder & CTO at HourOne

“Applied Materials is at the forefront of materials engineering solutions and leverages AI to deliver best-in-class products. We have been working with Deci on optimizing the performance of our AI model, and managed to reduce its GPU inference time by 33%. This was done on an architecture that was already optimized. We will continue using the Deci platform to build more powerful AI models to increase our inspection and production capacity with better accuracy and higher throughput.”

Amir Bar
Head of SW and Algorithm, Applied Materials

“Deci’s platform is suitable for both training and inference modes. Deci has advanced innovation in search for optimal neural network architectures. The solution excels in every area of our assessment.”

Michael Azoff
Chief Analyst, Kisaco Research

“With Deci, we increased by 2.6x the inference throughput of one of our multiclass classification models running on V100 machines – without losing accuracy. Deci can cut 50% off the inference costs in our cloud deployments worldwide.”

Chaim Linhart
CTO and Co-Founder, IBEX Medical Analysis

“The classification model I uploaded and integrated using Infery achieved a 33% performance boost, which is very cool for 10 minutes of work!”

Amir Zait
Algorithm Developer, Sight Diagnostics

“Deci delivers optimized deep learning inference on Intel processors as highlighted in MLPerf, allowing our customers to meet performance SLAs, reduce cost, decrease time to deployment, and gives them the ability to effectively scale.”

Monica Livingston
AI Solutions and Sales Director, Intel

“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 R&D, RingCentral

“By collaborating with Deci, we aim to help our customers accelerate AI innovation and deploy AI solutions everywhere using our industry-leading platforms, from data centers to edge systems that accelerate high-throughput inference.”

Arti Garg
Head of Advanced AI Solutions & Technologies, HPE
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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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