Samba Nova

Accelerated Computing with a Reconfigurable Dataflow Architecture

SambaNova DataScale systems and SambaFlow provide a unique vertically integrated platform that is optimized from the algorithm, through the compiler, and down to the silicon to achieve breakthrough performance and flexibility for innovators creating the next generation of machine-learning and deep-learning applications. Contact SambaNova to learn more about accelerating your AI and machine-learning applications.

SambaNova Systems DataScale

The Platform for Innovation. SambaNova Systems DataScale™ is an integrated system optimized for dataflow from algorithms to silicon. SambaNova DataScale is the core infrastructure for organizations that want to quickly build and deploy next generation AI technologies at scale.

SambaFlow

A complete software stack for SambaNova DataScale, SambaFlow™ is designed to advance developer productivity. Built to fully integrate with popular standard frameworks such as PyTorch and TensorFlow, SambaFlow provides an open, flexible, and easy-to-use development interface for exploring the new capabilities unlocked by DataScale.

Rethink What’s Possible: Pushing Computer Vision Boundaries Beyond 4K

Computer vision and image processing algorithms are increasingly common to commercial and research-related applications. These high resolution models perform complex computations to process image data in real-time and require rapid processing to yield analysis and results. In the context of machine learning image processing and analysis, resolution is everything. An image’s resolution can enable a

Breakthrough Efficiency in NLP Model Deployment

As Natural Language Processing (NLP) models evolve to become ever bigger, GPU performance and capability degrades at an exponential rate, leaving organizations across a range of industries in need of higher quality language processing, but increasingly constrained by today’s solutions. Throughout their lifecycles, modern industrial NLP models follow a cadence. They start from one-time task-agnostic