Drive Higher GPU utilization and throughput with Watson Machine Learning Accelerator

GPUs are designed and sized to run some of the most complex deep learning models such as RESNET, NMT, Transformer, DeepSpeech, and NCF. Most enterprise models being trained or deployed use only a fraction of the GPU compute and memory capacity. So, how do you reclaim this memory and compute headroom so that you can get the most out of your GPU investment? Watson Machine Learning Accelerator provides facilities to share GPU resources across multiple small jobs. This allows maximal return-on-investment for IT teams in enterprises where GPUs are in high demand. Additionally, you benefit from sharing a GPU across multiple jobs when your jobs are waiting for GPU resources or your distributed jobs running across GPUs might be stacked on top of each other on as few GPUs as possible to reduce the execution footprint.