Preprint proposes shared-memory architecture for language-model serving
The paper studies a hybrid architecture that pairs GPUs with processing-near-memory resources and shared DRAM for large language model inference. It focuses on prefill and decode phases, which stress compute and memory bandwidth differently when serving requests.
The paper studies a hybrid architecture that pairs GPUs with processing-near-memory resources and shared DRAM for large language model inference. It focuses on prefill and decode phases, which stress compute and memory bandwidth differently when serving requests.
The authors investigate whether sharing memory can reduce resource imbalance when the phases are separated. This is an architectural study, not a deployed service or a commercial processor announcement; performance claims should be read within the paper’s evaluated setup.
At the BUILD 2026 conference, the communications minister cited power, connectivity, cloud access and advanced chips as constraints on local AI growth.
Source: Voice of Nigeria, reporting the Ministry of Communications, Innovation and Digital Economy
Researchers from Ottawa, Columbia and Hanyang reviewed post-CMOS materials and device approaches for artificial neurons; the work is a literature review, not a new chip prototype.
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