You should know....

+=========+============+===============================+=============+=========================+=========================+
| version |  framework |        GPU architecture       |  precision  |  deployment to TensorRT |  throughput and latency |
+=========+============+===============================+=============+=========================+=========================+
| demo    |  pytorch   |  sm50                         |  fp32       |  NO                     |  nerfed                 |
+---------+------------+-------------------------------+-------------+-------------------------+-------------------------+
| full    |  pytorch   |  sm50, sm60, sm70, sm75, sm80 |  fp32, fp16 |  YES                    |  full capacity          |
+---------+------------+-------------------------------+-------------+-------------------------+-------------------------+
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End-User License Agreement ("Agreement")  

End-User License Agreement ("Agreement")  
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Last updated: April 11, 2024

Please read this End-User License Agreement carefully before clicking the "I Agree" button,
downloading or using the "pytorch embeddings module".

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Contact Us  
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Efficient Deep Learning Recommendation Models

Achieve a 39x reduction in hardware & electricity costs
Book a Call

How it works

Our trainable, compressed embedding layer significantly shrinks the memory footprint of large-scale neural networks. 

Imagine compressing massive DLRMs from hundreds of gigabytes down to just a few, enabling entire models to run on a single GPU. 

This not only streamlines operations by eliminating the need for GPU-to-GPU communication but also boosts throughput while barely affecting latency. Transform your DLRM deployment with our revolutionary solution.

Compressed Layer

Drop-in, trainable replacement layer for embedding layers in neural networks, without compromising accuracy.

High Compression Rate

Capable of compressing 420GB to 1GB without accuracy losses. The compression rate follows a logarithmic curve.

Simple Integration

Requires minimal code changes, facilitating easy adoption, maintenance and deployment into production.

Accuracy Preserved

Our advanced compression technology significantly reduces memory demands while maintaining the original accuracy of your neural network models.

39x Increased Throughput

Enhances processing capabilities, allowing companies to handle more data requests simultaneously.

Production Ready

Smaller neural networks are easier to manage and to deploy. No complex deployment strategies are required.

Lowering Environmental Impact

By significantly reducing the need for extensive hardware, we help cut down on energy consumption and carbon emissions. This allows your business to not only boost efficiency and reduce costs but also advance your sustainability goals.

Team

Tamas Hajgato
Founder & CTO

Lucas Bakx
Co-founder & CMO

Interested in learning more?

Discover how our solution can help you compress your embedding layers, allowing you to significantly save on hardware and electricity costs without compromising the performance of your recommendation systems.