ChatGPT, launched in 2022, began making a significant impact on the market by late 2023, according to Synergy Research Group. The company's chief analyst, John Dinsdale, points out that cloud market leaders have experienced accelerated revenue growth over time. Additionally, the emergence of numerous neocloud companies ( see box: What is a neocloud?) has further strengthened the already positive momentum in the market.
Intel is making a new push into GPUs, this time with a focus on data center workloads, as the chipmaker looks to reestablish itself in a market increasingly shaped by AI-driven demand and dominated by Nvidia. CEO Lip-Bu Tan said that after hiring a senior GPU architect, the company is working directly with customers to define requirements, signaling a more demand-driven approach as enterprises and cloud providers weigh their options for accelerated computing, according to a Reuters report.
The new capabilities center on two integrated components: the Dynamo Planner Profiler and the SLO-based Dynamo Planner. These tools work together to solve the "rate matching" challenge in disaggregated serving. The teams use this term when they split inference workloads. They separate prefill operations, which process the input context, from decode operations that generate output tokens. These tasks run on different GPU pools. Without the right tools, teams spend a lot of time determining the optimal GPU allocation for these phases.
In the early days, VAST Data's focus was primarily on storing enormous amounts of data. "Even before we talked about AI, data had to be stored somewhere," Pernsteiner notes. The company started out in the world of HPC (High Performance Computing). The choice of this sector was strategic: in that world, the scale and performance requirements are enormous. With this choice, VAST more or less forced itself to set the bar very high.
Scientists are showing that neuromorphic computers, designed to mimic the human brain, are not only useful for AI, but also for complex computational problems that normally run on supercomputers. This is reported by The Register. Neuromorphic computing differs fundamentally from the classic von Neumann architecture. Instead of a strict separation between memory and processing, these functions are closely intertwined. This limits data transport, a major source of energy consumption in modern computers. The human brain illustrates how efficient such an approach can be.
By replacing repeated fine‑tuning with a dual‑memory system, MemAlign reduces the cost and instability of training LLM judges, offering faster adaptation to new domains and changing business policies. Databricks' Mosaic AI Research team has added a new framework, MemAlign, to MLflow, its managed machine learning and generative AI lifecycle development service. MemAlign is designed to help enterprises lower the cost and latency of training LLM-based judges, in turn making AI evaluation scalable and trustworthy enough for production deployments.
What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.
