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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 · Data Center Knowledge English(EN) ·

    Scaling the Memory Wall: HBM, CXL, and the New GPU Playbook

    The AI industry is grappling with a significant 'memory wall' bottleneck, where GPU processing power outstrips memory bandwidth and capacity. This challenge is exacerbated by the increasing demands of training large generative AI models and the growing need for edge inference and agentic AI. Solutions like High Bandwidth Memory (HBM), Compute Express Link (CXL), and specialized on-processor SRAM meshes are being developed to address these limitations, though they introduce new challenges in supply, cost, and thermal management. AI

    Scaling the Memory Wall: HBM, CXL, and the New GPU Playbook

    IMPACT Addresses critical memory bottlenecks in AI infrastructure, impacting the cost and efficiency of training and inference.

  2. TOOL · Tom's Hardware English(EN) · · [3 sources]

    768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second

    A Redditor has successfully run a 1-trillion-parameter LLM, specifically Kimi K2.5, locally on a single GPU workstation by utilizing 768GB of second-hand Intel Optane Persistent Memory modules as RAM. This setup achieved approximately 4 tokens per second, a performance deemed impressive given the hardware's budget constraints. The use of discontinued Optane DIMMs highlights a potential market gap for affordable, high-capacity memory solutions for large language model inference, especially as DRAM prices fluctuate. AI

    768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second

    IMPACT Demonstrates a cost-effective method for running large LLMs locally, potentially influencing future hardware configurations for AI inference.