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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · arXiv cs.CV English(EN) · · [2 sources]

    DPU or GPU for Accelerating Neural Networks Inference -- Why not both? Split CNN Inference

    Researchers have developed a novel method for accelerating neural network inference by splitting Convolutional Neural Network (CNN) computations between Deep Learning Processing Units (DPUs) and Graphics Processing Units (GPUs). This 'Split CNN Inference' approach processes initial layers on a DPU near the data source and subsequent layers on a GPU, significantly reducing latency. A Graph Neural Network (GNN) model was also introduced to accurately predict optimal layer partitioning for various CNN architectures, achieving 96.27% accuracy. AI

    DPU or GPU for Accelerating Neural Networks Inference -- Why not both? Split CNN Inference

    IMPACT Potential for reduced latency in edge AI applications by optimizing hardware utilization for CNN inference.

  2. RESEARCH · Mastodon — mastodon.social Türkçe(TR) · · [2 sources]

    📰 RTX 2080 Ti VRAM Modding in 2026: Run Qwen 3.6 27B AI Model at 38 Tokens/Second Technology enthusiasts, upgrade your old RTX 2080 Ti GPUs to modern A

    An enthusiast has modified NVIDIA GeForce RTX 2080 Ti graphics cards to run the Qwen 3.6 27B AI model at 38 tokens per second. This setup utilizes older hardware, demonstrating that advanced AI inference is achievable with budget-friendly configurations. The modification involves increasing the VRAM on the cards to handle the substantial model. AI

    📰 RTX 2080 Ti VRAM Modding in 2026: Run Qwen 3.6 27B AI Model at 38 Tokens/Second Technology enthusiasts, upgrade your old RTX 2080 Ti GPUs to modern A

    IMPACT Shows that older, budget hardware can be modified for substantial AI model inference, potentially lowering the barrier to entry for local AI.