INDUSTRY APPLICATIONS
Many application workloads require the use of large datasets, and need rapid access to those datasets. We enable new classes of technologies which build on the use cases beginning to become commonplace today.
WHICH APPLICATIONS BENEFIT?
01. AI & MACHINE LEARNING
Large AI model development and testing requires massive memory footprints that traditional servers cannot provide. Our Memory-as-a-Service (MaaS) platform enables startups and enterprises to lease high-performance AI machines with scalable memory on demand.
Both model training and inferencing can require the use of very large datasets, and each benefit from the ability to process that data faster. Even the fastest AI accelerator can only process data which it has fetched from memory; where memory is either too small or too slow, very expensive accelerator resources can be left idle.
Whether CPU, GPU, TPU or another accelerator; they can all benefit from a fast and disaggregated memory bank in the rack which performs just like their own local system memory.
02. BIG DATA ANALYTICS
Much of the data generated across the world today is analyzed not just by itself, but in the context of other data in order for businesses to extract more valuable insights from it than has been possible in the past. Sentiment analysis of social media activity, transaction analysis for credit card processing and real-time financial market analysis are all examples of valuable use cases which stand to be effectively accelerated.
With TORmem, the very large datasets used for these applications can be kept in fast, disaggregated memory, making large-scale in-memory databases a reality.
03. SUPERCOMPUTER DEMOCRATIZATION
Although it may sound a little hyperbolic, in many cases the primary differentiator between a supercomputer and the large server clusters of an enterprise or a hyperscale cloud is in their memory architecture. By bringing high-speed, economical banks of disaggregated memory to the data center, TORmem is closing the gap.
Tasks such as weather simulation, structural analysis and climate modelling can become more possible and economical with our memory technology at scale.
ACCELERATING CURRENT WORKLOADS
Many current workloads can also be accelerated by the addition of disaggregated memory. Whether in new or existing compute systems, it can be challenging to balance the need for more memory to keep CPUs, GPUs and other compute infrastructure fed with data, with the capacity of servers and the inflexibility and cost that comes with loading up individual servers with large quantities of RAM. TORmem's approach is to add a bank of high-speed disaggregated memory to the rack, shared between multiple servers.