It\u2019s easy to focus on the GPUs when building an AI platform. That\u2019s where the budget goes, and that\u2019s where the performance shows up in the specs. But a GPU can only work as fast as the data reaches it. If storage capacity isn\u2019t proportional to computing power, the result is a cluster that looks impressive on paper but, in practice, spends a large portion of its time waiting.<\/p>\n
We often see this in organizations that have invested in compute resources and then connected them to existing storage. Training jobs take longer than expected, GPU utilization is lower than it should be, and no one can quite pinpoint why. In this article, we\u2019ll explore why AI places different demands on storage, what a parallel file system offers, and how you can prevent I\/O from becoming the limiting factor.<\/p>\n
A GPU that is waiting for data still consumes power, takes up space in the rack, and is depreciated at the same rate as a GPU that is in use. The difference is that it doesn’t produce any output.<\/p>\n
A simple calculation shows how quickly costs can add up. If a cluster with 64 GPUs spends 30 percent of its time waiting for I\/O, that\u2019s equivalent to about 19 GPUs that aren\u2019t doing anything useful. That\u2019s several servers that, in practice, might as well be turned off.<\/p>\n
The tricky part is that the problem rarely looks like a storage issue. Instead, it feels like training takes too long, queues are long, or the cluster is simply too small. The natural conclusion is to buy more GPUs, but if storage is already the bottleneck, the wait will only get longer.<\/p>\n
Traditional enterprise storage is designed for databases, virtual machines, and file servers. It handles many small, predictable transactions well and provides low latency for individual users. AI workloads operate in a completely different way.<\/p>\n
During training, hundreds of GPUs often read from the same dataset at the same time, and they all want full bandwidth at once. The data may consist of millions of small files\u2014such as images or documents\u2014that are read in random order. When training large language models, however, the focus is on large sequential reads. An AI platform needs to be able to handle both.<\/p>\n
On top of that, there\u2019s checkpointing. Large models regularly save their state, and in the meantime, the entire cluster sits idle. The longer a checkpoint takes, the more GPU time is lost. Furthermore, when the dataset has grown larger than what can fit in local memory or on local NVMe drives, data must be retrieved from shared storage during each pass.<\/p>\n
In production, the pattern changes again. Inference and RAG require that models load quickly and that context can be retrieved with low latency, often for many users simultaneously.<\/p>\n
In this scenario, a traditional NAS with just a few controllers becomes a bottleneck. It doesn’t matter how many disks are connected to it; all traffic must still pass through the same point.<\/p>\n
A parallel file system distributes both data and metadata across many storage nodes. Instead of queuing through a single controller, each GPU server can read from multiple nodes simultaneously. When you add nodes, not only does capacity increase, but bandwidth does as well.<\/p>\n
Metadata is an aspect that is often overlooked. When a dataset consists of millions of files, operations such as opening, listing, and checking files become a significant burden. If all metadata is managed in one place, the bottleneck simply shifts there.<\/p>\n
The third piece of the puzzle is how the data gets to the GPU. With RDMA and NVIDIA GPUDirect Storage, data can travel directly from storage to GPU memory, without passing through the CPU or system memory. This results in lower latency and frees up CPU capacity for data preprocessing.<\/p>\n
The network deserves the same attention. A parallel file system can never be faster than the network that supports it. That is why, in a reference architecture such as the NVIDIA DGX SuperPOD, the storage network is sized just as carefully as the network between the GPU nodes.<\/p>\n
Most issues can be prevented if storage is planned in conjunction with the rest of the platform. These are the principles we usually follow:<\/p>\n
VAST Data and WEKA are among the most established platforms for AI storage, but they approach the problem in different ways. There are also other options that may be the right choice depending on the environment and scale.<\/p>\n
The best platform for your needs depends on your workloads, your scale, your network, and who will be operating the solution. The licensing model, support, and how well the platform fits into your existing environment are often just as important as pure performance.<\/p>\n
| Platform<\/th>\n | Architecture<\/th>\n | Strength<\/th>\n | Suitable for<\/th>\n<\/tr>\n<\/thead>\n |
|---|---|---|---|
| VAST Data<\/td>\n | Disaggregated all-flash, shared-everything<\/td>\n | Brings together file, object, and data into a single, large-scale platform<\/td>\n | You want a data lake, training, and inference all in one place<\/td>\n<\/tr>\n |
| WEKA<\/td>\n | Software-defined parallel file system on NVMe<\/td>\n | Very low latency, excels with small files, on-premises and in the cloud<\/td>\n | Performance per GPU is crucial, or you\u2019ll run a hybrid setup<\/td>\n<\/tr>\n |
| DDN EXAScaler<\/td>\n | Lustre-based parallel file system<\/td>\n | High sequential bandwidth, long history in HPC<\/td>\n | Large-scale research and HPC environments<\/td>\n<\/tr>\n |
| IBM Storage Scale<\/td>\n | Parallel File System (formerly GPFS)<\/td>\n | A mature solution with comprehensive enterprise features<\/td>\n | You have an IBM environment or strict data management requirements<\/td>\n<\/tr>\n |
| Pure Storage FlashBlade<\/td>\n | Scale-out all-flash for file and object storage<\/td>\n | Easy to operate and predictable performance<\/td>\n | Small and medium-sized clusters where simplicity is a top priority<\/td>\n<\/tr>\n |
| Lustre \/ BeeGFS (open source)<\/td>\n | Parallel file system<\/td>\n | Low licensing cost<\/td>\n | You have your own expertise for operation and troubleshooting<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nHere’s how you can tell if the cluster is starving<\/h2>\nThere are some clear signs that your storage is holding back your AI platform:<\/p>\n
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