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

One big selling point of Rubin is dramatically lower AI inference costs. Compared to Nvidia's last-gen Blackwell platform, ...

Until now, AI services based on large language models (LLMs) have mostly relied on expensive data center GPUs. This has ...

A new test-time scaling technique from Meta AI and UC San Diego provides a set of dials that can help enterprises maintain the accuracy of large language model (LLM) reasoning while significantly ...

SOUN's hybrid AI model blends speed, accuracy, and cost control-outpacing LLM-only rivals in real-world deployments.

The next major evolution will come from multi-agent systems—networks of smaller, specialized AI models that coordinate across ...

Qdrant, the leading provider of high-performance, open-source vector search, today announced the launch of Qdrant Cloud Inference, a fully managed service that enables developers to search both text ...

A research article by Horace He and the Thinking Machines Lab (X-OpenAI CTO Mira Murati founded) addresses a long-standing issue in large language models (LLMs). Even with greedy decoding bu setting ...

Semantic caching is a practical pattern for LLM cost control that captures redundancy exact-match caching misses. The key ...

Cloudflare’s NET AI inference strategy has been different from hyperscalers, as instead of renting server capacity and aiming to earn multiples on hardware costs that hyperscalers do, Cloudflare ...