{
    "success": true,
    "data": {
        "id": 1713032,
        "msgid": "google-prepares-cure-for-memory-crisis-ram-hoarders-in-panic-1777639913",
        "date": "2026-05-01 19:09:00",
        "title": "Google Prepares \"Cure\" for Memory Crisis, RAM Hoarders in Panic",
        "author": "Wahyunanda Kusuma Pertiwi",
        "source": "KOMPAS",
        "tags": "",
        "topic": "Technology",
        "summary": "Google is developing TurboQuant, an AI-based memory compression algorithm designed to alleviate the ongoing memory crisis in the tech industry amid soaring RAM prices driven by AI demands. By significantly reducing memory usage during AI inference without sacrificing accuracy, this innovation could ease supply constraints and lower costs for consumers and businesses alike. The technology combines PolarQuant and Quantization-aware Joint Learning to achieve up to sixfold memory savings, potentially reshaping efficiency in AI deployments.",
        "content": "<p>Google is developing a new technology called TurboQuant. This\ntechnology is an AI-based memory compression algorithm, touted as a\n\u201ccure\u201d for the memory crisis currently plaguing the industry.<\/p>\n<p>Amid the surge in RAM prices due to high AI computing needs, this\ninnovation is seen as having the potential to relieve pressure in an\nunconventional way: by making AI require far less memory.<\/p>\n<p>TurboQuant was developed by the company\u2019s research division, Google\nResearch, with a primary focus on memory efficiency during the inference\nprocess, when AI models are run rather than trained.<\/p>\n<p>TurboQuant\u2019s operation relies on a technique called vector\nquantisation, a method of simplifying numerical data representations in\nvector form to make them more compact without losing important\ninformation.<\/p>\n<p>With this approach, data that previously required large space can be\ncompressed significantly while still maintaining the AI model\u2019s\naccuracy.<\/p>\n<p>Technically, TurboQuant relies on two main methods: PolarQuant and\nQuantisation-aware Joint Learning (QJL).<\/p>\n<p>PolarQuant functions by changing the way data is represented to make\nit more efficient when stored in memory, without sacrificing\ncomputational quality.<\/p>\n<p>Meanwhile, QJL trains the AI model to be \u201caware\u201d that the processed\ndata will be compressed, allowing the model to adapt and still produce\naccurate outputs even when working with compacted data.<\/p>\n<p>With the combination of these two techniques, researchers claim\nTurboQuant can save up to six times more memory usage compared to\nconventional methods.<\/p>\n<p>This means AI models can \u201cremember\u201d more information in a much\nsmaller space, while also reducing performance barriers due to memory\nlimitations.<\/p>\n<p>In recent times, memory prices, especially DDR5, have skyrocketed due\nto high demand from the AI industry.<\/p>\n<p>Memory manufacturers are prioritising supplies for large-scale data\ncentres (hyperscalers), making availability for consumer markets like\nPCs and laptops limited.<\/p>\n<p>As a result, global RAM prices have surged up to four to five times\ncompared to previous normal conditions.<\/p>",
        "url": "https:\/\/jawawa.id\/newsitem\/google-prepares-cure-for-memory-crisis-ram-hoarders-in-panic-1777639913",
        "image": ""
    },
    "sponsor": "Okusi Associates",
    "sponsor_url": "https:\/\/okusiassociates.com"
}