Efficient Large Language Models

Reduce your hardware & electricity costs
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How it works

Our attention mechanism is a drop-in replacement of the scaled dot-product attention found in large language models.

Imagine being able to pre-train large language models within a more reasonable budget, without having to worry about the attention fading problem that comes with large context lengths.

True linear attention - both the memory and compute requirements scale linearly - in both the token dimension and the context length.

Attention Layer

Drop-in replacement for the scaled dot-product attention mechanism, without compromising accuracy.

High Compression Rate

No hidden costs - both the compute and memory requirements scale linearly instead of quadratically.

Simple Integration

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

Accuracy Preserved

Our advanced compression technology significantly reduces compute and memory demands while maintaining the original accuracy.

Increased Throughput

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

Production Ready

Ready to be used with fully sharded data parallel training.
Ready to be deployed into TensorRT format.
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 in training large language models, allowing you to significantly save on hardware and electricity costs without compromising the performance of your neural networks.
Book a Call