Over the past three years, Volcano Engine president Tan Dai has repeated the same cycle when setting revenue targets for his ...
The companies attributed this speed to a deep software-hardware co-development process that actively used OpenAI’s own models ...
AI inference infrastructure investment pulled $1.8 billion in 48 hours as Baseten’s $1.5B round at a $13B valuation and ...
Two B-52 bombers will head back to their manufacturer for new engines this year, kicking off a long-awaited upgrade meant to help keep flying the Stratofortress until nearly their 100th birthday. On ...
Built alongside early design partners, the Inference Engine gives AI developers unified control over performance, cost, and scale — with customers reporting up to 67% lower inference costs. Inference ...
AI-native startups report 50% faster training cycles and 40% decrease in latency when running production AI on DigitalOcean. DigitalOcean (NYSE: DOCN), the Agentic Inference Cloud built for production ...
An open standard for AI inference backed by Google Cloud, IBM, Red Hat, Nvidia and more was given to the Linux Foundation for stewardship in further proof training has been superseded by inference in ...
Amazon Web Services plans to deploy processors designed by Cerebras inside its data centers, the latest vote of confidence in the startup, which specializes in chips that power artificial-intelligence ...
Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running. For neocloud operators, those empty cycles are lost margin.
This is a python package focused on systems performance: quantized weights, KV cache reuse, dynamic batching, token streaming, and rigorous benchmarking across backends. llminfer is for engineers who ...
Adding big blocks of SRAM to collections of AI tensor engines, or better still, a waferscale collection of such engines, turbocharges AI inference, as has been shown time and again by AI upstarts ...
Much of the conversation around AI today is focused on building cloud capacity and massive data centers to run models. Companies like Apple and Qualcomm are in the early stages of making on-device AI ...