Browsing: attention

Linear attention replaces the unbounded KV cache of softmax attention with a fixed-size recurrent state. This cuts sequence mixing to linear time and decoding to constant…

This is Behind the Blog, where we share our behind-the-scenes thoughts about how a few of our top stories of the week came together. This week,…

Training large language models on long sequences has a well-known problem: attention is expensive. The scaled dot-product attention (SDPA) at the core of every transformer scales…

Gadget Weekly (Image credit: Android Central)Join Namerah Saud Fatmi as she explores the cool, quirky, and sometimes downright odd world of smartphone accessories, gadgets, and other…

The team behind Kimi.ai (Moonshot AI) just made a significant contribution to the open-source AI infrastructure space. The research team has made a significant contribution to…

The race to make large language models faster and cheaper to run has largely been fought at two levels: the model architecture and the hardware. But…

DeepSeek-AI has released a preview version of the DeepSeek-V4 series: two Mixture-of-Experts (MoE) language models built around one core challenge making one-million-token context windows practical and…

Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math…

Toyota has built a serious reputation for SUVs that deliver. The RAV4 set new standards for compact crossovers, the Land Cruiser dominates off-road adventures, the 4Runner…

When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data.…