Cut Your LLM Compute Cost
with Faster Inference

Optimize your generative models to reduce inference cost and deliver better user experience. Easily optimize your models to gain cost-efficient inference without compromising accuracy with Deci’s inference acceleration tools.

The Challenge

Extremely large models and variable inference costs means that your generative AI applications come at a significantly high operational cost. As your inference scales, so does your cloud bill.

Cut Cloud ML Spend with Faster Inference

Lower your cloud bill by maximizing the throughput of your models.

Migrate Workloads to More Affordable Hardware

Run your models on affordable and widely available GPUs by improving inference efficiency.

Improve User Experience with Better Inference Speed

Improve inference speed without compromising on accuracy.

Effectively Deploy and Scale Your Generative AI Models


Inference Acceleration on Average


Model Size Reduction on Average


Cloud Cost Reduction

See It In Action

Play Video

How Does It Work?

Get Similar Results for Your Specific Use Case

Improving UX and Reducing Cloud Costs for Image Generation

A company offering a video generation application wasn’t achieving the desired throughput to support its offering. The application is powered by a GAN model for image generation.

The customer leveraged Deci’s compilation and quantization tool to optimize the latency and achieve a 2.1x boost. As a result, the customer was able to process its videos on less machines and hence reduce cloud cost by 40%.

The boost in performance enabled the customer to deliver better use experience as well as reduce the cloud compute costs.

"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
Hour One

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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