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.

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

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 ...

A new technical paper titled “Making Strong Error-Correcting Codes Work Effectively for HBM in AI Inference” was published by researchers at Rensselaer Polytechnic Institute, ScaleFlux and IBM T.J.

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