Use of accelerators is growing in line with the growth of AI. These accelerators are specialized chips that are neither CPUs nor GPUs – they specifically offload and speed up the precise workloads required for AI. Primary producers include Tenstorrent, Groq, Cerebras, Graphcore, and Google.
Neo-clouds are new but increasingly available specialized clouds distinct from the traditional class of hyperscalers such as Azure, Google Cloud and AWS. They are AI-first, often built with accelerators, and well-suited for customers requiring AI training, inference and model building services. Neo-clouds provide massive parallelism, low-latency edge compute, flexible deployment and cheaper or more predictable economics. Example neo-clouds include CoreWeave and Nebius.
Typical AI use of neo-clouds includes training large-scale AI models and running high-throughput AI inference. In the latter, for example, the customer will use the neo-cloud’s accelerator-optimized low latency hardware to ensure rapid response times for in-house developed chatbots.
But accelerators, and therefore neo-clouds, suffer from several security blind spots. Traditional cybersecurity tools have been developed over decades around CPU-centric operating systems. They haven’t kept pace with the emergence of accelerators, and they lack visibility into the accelerators’ high-speed video memory. Current cybersecurity cannot readily detect what is happening within neo-cloud hardware.
