AI Training and Inference is very inefficient today incurring huge costs with real impact on the environment. We live in the Jurassic era of A.I efficiency and Nod.AI believes we can do much better with a solid focus on our Computer Science fundamentals with first principles thinking. We deliver the best Neural Compiler frontend for various Machine Learning frameworks like Tensorflow, PyTorch etc which can very efficiently parallelize and distribute the workloads onto the Nod Runtime for auto-tuned high performance execution on large clusters and SoCs. We effectively deploy compiled A.I models onto a wide range of hardware platforms – from supercomputers with thousands of devices to System-on-Chip designs
We enable companies to efficiently deploy Machine Learning models without doing the heavy lifting of optimizing the frameworks and models for their hardware and clusters. Training advanced deep learning models is challenging, this gets exponentially harder when you are optimizing for various criteria such as target devices, latency and training time etc. Larger NLP models where data parallelism isn’t a viable option any more needs custom modifications to the model to fit your training framework and infrastructure. Sign up for our Early Access on AWS, GCP or on-premise and let us show you how we can save you money on ML Training costs.