OCF is able to supply the latest NVIDIA Tesla T4, Tesla V100, Tesla V100S and Quadro RTX6000 GPU’s hosted on NVIDIA OEM servers, along with dedicated NVIDIA DGX Station, NVIDIA DGX-1, NVIDIA DGX A-100 Deep Learning platforms and machine learning optimised storage solutions.
If you are interested in a NVIDIA workstation platform, OCF also offers the NVIDIA DGX Station with 4x NVIDIA Tesla V100 GPU's. This integrated hardware and software solution allows your data science team to easily access a comprehensive catalogue of NVIDIA, optimised, GPU-accelerated, containers, offering the fastest possible performance for AI and data science workloads.
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If you are looking to house your AI systems in a data centre, OCF has a number of AI capable servers which can support 1-8 of the latest NVIDIA A100, A10, A30, A40, Tesla T4, Tesla V100, Tesla V100S and Quadro RTX6000 GPU’s, some of which include NVIDIA NVLink interconnect to give the best GPU to GPU, and in the case of IBM Power Systems, GPU to CPU performance.
OCF also offers Graphcore IPUs for AI infrastructure at scale the products include IPU-M2000, IPU-POD16, IPU-POD64 and Graphcloud.
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Keeping the latest GPU solutions for machine learning and deep learning workloads fed with data can be a challenge, even for the highest performance storage solutions.
OCF partners with specialist storage vendors including DDN, IBM and Netapp to offer storage solutions designed, optimised and verified specifically for NVIDIA DGX-1 DGX A-100, NVIDIA DGX-2 and other GPU heavy AI compute workloads, to allow you make the most of this investment and reduce the time to results.
Partnering with leading Cloud providers OCF offers the flexibility, agility, and security of a fully-managed cloud software service. The needs of scale-up during training and connectivity of remote devices can all be achieved via OCF’s Cloud AI Platforms
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OCF combines rugged embedded and Edge computers, computational power and IoT components to enable Edge AI, enabling Deep Learning and Machine Learning with High Performance Edge Computers. They are able to process data autonomously and perform Machine Learning (ML) in the field and apply Deep learning (DL) models and algorithms for advanced autonomous applications.
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