SGLang, which originated as an open source research project at Ion Stoica’s UC Berkeley lab, has raised capital from Accel.

The next generation of inference platforms must evolve to address all three layers. The goal is not only to serve models ...

The vast proliferation and adoption of AI over the past decade has started to drive a shift in AI compute demand from training to inference. There is an increased push to put to use the large number ...

AMD (AMD) is rated a 'Buy' based on its architectural strengths and plausible 3-5 year EPS growth framework. AMD’s higher memory bandwidth and capacity position it well for the rapidly compounding ...

Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...

Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...

The market for serving up predictions from generative artificial intelligence, what's known as inference, is big business, with OpenAI reportedly on course to collect $3.4 billion in revenue this year ...

Until recently, most AI was in data centers/cloud and most of that was training. Things are changing quickly. Projections are AI sales will grow rapidly to tens of billions of dollars by the mid 2020s ...