MinIO is capable of the performance needed to feed your hungry GPUs; a recent benchmark achieved 325 GiB/s on GETs and 165 GiB/s on PUTs.
A chain is as strong as its weakest link - and your AI/ML infrastructure is only as fast as your slowest component. If you train machine learning models with GPUs, then your weak link may be your storage solution. The result is what I call the “Starving GPU Problem.” The Starving GPU problem occurs when your network or your storage solution cannot serve training data to your training logic fast enough to fully utilize your GPUs. The symptoms are fairly obvious.
The new platform uses the , which can be connected with additional Superchips by , allowing them to work together during model training and inference. announced Grace Hopper Superchip NVIDIA NVLink While all the specifications on the Grace Hopper Superchip represent an improvement over previous chips, the most important innovation for AI/ML engineers is its unified memory. Grace Hopper gives the GPU full access to the CPU’s memory.
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