Collector facilitates installation of transformer
District Collector Hanumanth Rao, along with Additional Collector (Local Bodies) Gangadhar, conducted a surprise inspection ...
Nairametrics2 小时
Abuja residents to experience power outages as TCN schedules transformer maintenance
The Transmission Company of Nigeria (TCN) has announced that there will be power outages in parts of Abuja this weekend ...
IEEE2 天
Analysis of Transformer Decoder Architecture and KV Cache Behavior During LLM Inference
This Transformer-based model has become the standard not only in language processing ... We also examine the rationale behind the existence of the KV caching methodology and how it operates.
Multibagger stock Transformers & Rectifiers locked in 5% upper circuit for 2nd straight ...
TRIL shares surged to ₹405, locked at the 5% upper circuit limit, following a ₹166.45 crore order. The company reported a 52% ...
腾讯网1 天
LLM高效推理：KV缓存与分页注意力机制深度解析
随着大型语言模型（LLM）规模和复杂性的持续增长，高效推理的重要性日益凸显。KV（键值）缓存与分页注意力是两种优化LLM推理的关键技术。本文将深入剖析这些概念，阐述其重要性，并探讨它们在仅解码器（decoder-only）模型中的工作原理。 冗余计算 ...
Sandhills transformer facility expansion aided by performance grant
North Carolina’s Commerce Department is supporting a Pennsylvania company’s expansion adding more than 200 jobs in the ...
腾讯网1 天
华泰证券：DeepSeek有望加速模型训练与CUDA解耦
结合xAI发布的Grok-3，xAI已经将10万卡集群扩展到20万，确实带来了当下全球最领先的预训练/推理模型性能。对比xAI和DeepSeek，10万卡vs万卡，Grok-3相比R1在某些测评集上提高了20%左右效果，是否有性价比？认为，这并不冲突 ...
Transformers & Rectifiers clock 5% upper circuit on bagging Rs 166-cr order
The company secured the order from Hyosung T&D India and the delivery is scheduled for the next financial year ...
Zawya3 天
Linxon awarded new 400 kV grid station for Industrial City of Abu Dhabi
Linxon has been selected by TAQA Transmission, to execute a full turnkey EPC 400/220 kV substation at ICAD-4 in the United ...
DeepSeek创新技术引领AI算力新突破：加速模型训练与CUDA解耦
华泰证券指出，DeepSeek的技术路径代表了国内AI发展的新方向，即在有限算力条件下，通过算法和硬件的极致优化，实现更高的模型性能。这一思路不仅提升了计算效率，也为AI技术的普及和应用开辟了新路径。
DeepSeek NSA：革新Transformer，推动AI训练与硬件解耦
近年来，人工智能技术的迅猛发展引发了学术界与产业界的广泛关注。其中，DeepSeek发布的NSA（原生稀疏注意力）算法为Transformer架构的Attention环节带来了显著的优化，尤其在训练速度和解码效率上，显示出与传统Full Attention的强大竞争力。NSA不仅在效果上与Full Attention持平，甚至在某些场景下表现出色，关键在于其利用稀疏KV（键值）的方法实现了速度提升 ...