Bitfusion provides software that makes managing and using Deep Learning and AI infrastructure easy, elastic, and efficient.

Deep Learning and other advanced machine learning techniques demand a complex tool chain and a very compute intensive, time intensive analysis process. GPUs can speed up training times, but managing both the infrastructure and software for GPUs creates huge productivity challenges. Bitfusion provides a GPU virtualization and application management platform that accelerates applications and training time with no code changes, and makes it easy to efficiently manage production GPU clusters with high availability, team multi-tenancy, and parallel job execution. Develop algorithms more easily, transition from prototyping to large-scale, multi-GPU training, and gain peace of mind that your training is resilient to hardware failure and other common infrastructure headaches.

Application performance demands are outpacing Moore’s Law and modern data centers are getting comprised of heterogeneous hardware systems beyond CPUs. Traditional approaches in scale-out computing or application rewrites are expensive propositions for most people. Tackling the time and cost challenge, meeting performance demands and taking advantage of hardware (GPU, FPGA) based innovations to maximize R&D investments requires a fundamentally different approach to computing. Bitfusion addresses this huge gap in the market today by providing innovative software that allows developers and data scientists to accelerate their applications and take advantage of diverse hardware, and makes it far easier for IT organizations to manage a heterogeneous data center simply and efficiently.

Deep learning and artificial intelligence have previously only been possible at significant scale at a select few organizations. At Bitfusion, we’re completely changing that dynamic, enabling all organizations, data scientists, and developers to leverage deep learning software and high-performance hardware like GPUs quickly, productively, and cost-effectively.