{"id":36372,"date":"2026-07-06T01:25:43","date_gmt":"2026-07-05T23:25:43","guid":{"rendered":"https:\/\/www.chinasmartbuy.com\/blog\/kim-jeong-ho-ai-memoria-gpu-hbm-hbf\/"},"modified":"2026-07-06T01:25:45","modified_gmt":"2026-07-05T23:25:45","slug":"kim-jeong-ho-ai-memoria-gpu-hbm-hbf","status":"publish","type":"post","link":"https:\/\/www.chinasmartbuy.com\/blog\/kim-jeong-ho-ai-memoria-gpu-hbm-hbf\/","title":{"rendered":"AI, il padre dell\u2019HBM: la GPU lavora davvero solo per il 10%-30%"},"content":{"rendered":"\n\n<style>\n  :root {\n    --red: #CC1B1B;\n    --red-light: rgba(204,27,27,0.08);\n    --red-mid: #e84040;\n    --accent-soft-bg: rgba(204,27,27,0.07);\n    --accent-soft-border: rgba(204,27,27,0.18);\n    --text: #1a1a1a;\n    --text-muted: #666;\n    --card: #ffffff;\n    --border: #ebebeb;\n    --page-bg: #ffffff;\n    --surface-soft: #fafafa;\n    --surface-contrast: #f0f0f0;\n    --line-soft: #f5f5f5;\n    --soft-label: #bbb;\n    --muted-label: #999;\n    --copy: #333;\n    --font-head: 'Bebas Neue', sans-serif;\n    --font-body: 'DM Sans', sans-serif;\n    --heading-spacing: 0.04em;\n    --heading-weight: 400;\n    --spec-header-spacing: 0.08em;\n    --quickfacts-shadow: 0 10px 24px rgba(15,23,42,0.06);\n  }\n  .fbp5-wrap,.fbp5-wrap * { box-sizing:border-box; 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}\n    .rh-downloadbox-link { width:100%; }\n    .rh-offerbox-head { flex-direction:column; }\n    .rh-offerbox-links { grid-template-columns:1fr; }\n    .rh-offer-inline-wrap { flex-direction:column; align-items:flex-start; }\n    .rh-offer-inline-cta { width:100%; }\n  }\n\n  \/* RH_FBP5_CARDS_CSS_V1 *\/\n  .fbp5-related-grid { display:grid; grid-template-columns:repeat(3,1fr); gap:14px; }\n  .fbp5-telegram-grid { grid-template-columns:repeat(2,1fr); }\n  .fbp5-related-card { display:block; overflow:hidden; border:1px solid var(--border,#ebebeb); border-radius:8px; background:var(--card,#fff); color:inherit !important; text-decoration:none !important; transition:transform .18s ease, box-shadow .18s ease, border-color .18s ease; }\n  .fbp5-related-card:hover { transform:translateY(-2px); border-color:rgba(204,27,27,.35); box-shadow:0 16px 36px rgba(18,18,18,.10); }\n  .fbp5-related-card img { display:block; width:100%; aspect-ratio:16\/9; object-fit:cover; background:var(--surface-soft,#fafafa); }\n  .fbp5-related-thumb-placeholder { width:100%; aspect-ratio:16\/9; display:flex; align-items:center; justify-content:center; background:linear-gradient(135deg,rgba(204,27,27,.10),rgba(204,27,27,.03)); color:var(--red,#CC1B1B); font-family:var(--font-head,'Bebas Neue',sans-serif); font-size:28px; letter-spacing:.04em; }\n  .fbp5-related-body { padding:12px; }\n  .fbp5-related-body span { display:block; margin:0 0 6px; color:var(--red,#CC1B1B); font-size:11px; font-weight:900; letter-spacing:.08em; text-transform:uppercase; }\n  .fbp5-related-body strong { display:block; font-size:15px; line-height:1.25; color:var(--text,#1a1a1a); }\n  .fbp5-related-body small { display:block; margin:7px 0 0; color:var(--text-muted,#666); font-size:13px; line-height:1.35; }\n  @media(max-width:720px) {\n    .fbp5-telegram-grid { grid-template-columns:1fr; }\n    .fbp5-related-grid:not(.fbp5-telegram-grid) { grid-template-columns:1fr; }\n    .fbp5-related-grid:not(.fbp5-telegram-grid) .fbp5-related-card { display:grid; grid-template-columns:112px minmax(0,1fr); align-items:stretch; }\n    .fbp5-related-grid:not(.fbp5-telegram-grid) .fbp5-related-card img,\n    .fbp5-related-grid:not(.fbp5-telegram-grid) .fbp5-related-thumb-placeholder { height:100%; min-height:92px; aspect-ratio:auto; }\n  }\n<\/style>\n<div class=\"fbp5-wrap rh-json-article\" id=\"rh-article-mr8f5v5m-xvlv7\" data-rh-article-type=\"news\" data-rh-site-profile=\"china-smart-buy\">\n  <div class=\"fbp5-intro\">\n    \n    <p>Kim Jeong-ho, docente del KAIST noto come il \u201cpadre dell\u2019HBM\u201d, sostiene che nella fase attuale dell\u2019AI il collo di bottiglia non sia pi\u00f9 soltanto la GPU. Il punto centrale, sempre pi\u00f9, \u00e8 la memoria: capacit\u00e0, larghezza di banda e velocit\u00e0 con cui i dati vengono spostati e resi disponibili al calcolo.<\/p><p>La tesi \u00e8 netta: nell\u2019inferenza AI le GPU lavorano davvero solo per <strong>il 10%-30% del tempo<\/strong>, mentre il resto si perde nel trasferimento dei dati tra memoria e processore. \u00c8 un cambio di prospettiva importante, perch\u00e9 sposta l\u2019attenzione dalle sole unit\u00e0 di calcolo all\u2019intera architettura di memoria che alimenta i carichi AI.<\/p>\n  <\/div><figure class=\"wp-block-image size-large\" style=\"margin:18px 0;display:block;clear:both;width:100%;position:relative;z-index:1\"><a href=\"https:\/\/www.chinasmartbuy.com\/blog\/wp-content\/uploads\/2026\/07\/ai-il-padre-dell-hbm-de43aa1e-59c1-4a26-abed-8b93-1sxs0wrb78.webp\" target=\"_blank\" rel=\"noopener\" style=\"display:block;width:100%;position:relative;z-index:2;pointer-events:auto;cursor:zoom-in\"><img decoding=\"async\" src=\"https:\/\/www.chinasmartbuy.com\/blog\/wp-content\/uploads\/2026\/07\/ai-il-padre-dell-hbm-de43aa1e-59c1-4a26-abed-8b93-1sxs0wrb78.webp\" alt=\"HBM \u4e4b\u7236\u91d1\u6b63\u6d69\uff1aAI \u7684\u672c\u8d28\u662f\u5185\u5b58\uff0cGPU \u771f\u6b63\u5de5\u4f5c\u7684\u65f6\u95f4\u53ea\u6709 10%-30%\" loading=\"lazy\" style=\"display:block;max-width:100%;height:auto;margin:0 auto;pointer-events:auto\"><\/a><\/figure>\n  \n\n      <section class=\"fbp5-section\">\n        <div class=\"fbp5-section-label\">Scenario<\/div>\n        <h2>Perch\u00e9 nell\u2019inferenza AI la memoria sta diventando pi\u00f9 importante della <span>sola GPU<\/span><\/h2>\n        <p>L\u2019idea di Kim Jeong-ho parte da un dato semplice: ogni risposta generata da un sistema AI richiede prima la lettura dei dati dalla memoria HBM, poi l\u2019elaborazione sulla GPU e infine la scrittura del risultato di nuovo in memoria. In questa catena, una parte molto ampia del tempo non viene spesa per il calcolo puro, ma per il trasporto dei dati.<\/p><p>Per questo, anche aumentando drasticamente il numero di acceleratori installati, l\u2019efficienza reale non cresce in modo lineare. Se la GPU resta in attesa dei dati, la potenza teorica disponibile non si traduce automaticamente in prestazioni concrete. \u00c8 qui che memoria, banda passante e capacit\u00e0 iniziano a contare quanto, se non pi\u00f9, della sola potenza di calcolo.<\/p><p>Kim distingue anche tra il passato recente dell\u2019AI e la fase che si sta aprendo adesso. Nel periodo dominato dall\u2019addestramento, la GPU era il fulcro del sistema; nell\u2019era dell\u2019<strong>inferenza AI<\/strong>, invece, il fattore decisivo diventa quanta informazione si pu\u00f2 gestire e con quale rapidit\u00e0 la si pu\u00f2 fornire al motore di calcolo.<\/p>\n        <ul class=\"fbp5-bullet-list\"><li>Utilizzo reale della GPU nell\u2019inferenza: <strong>10%-30%<\/strong>.<\/li><li>Il tempo restante viene assorbito soprattutto da lettura, trasferimento e scrittura dei dati.<\/li><li>La prestazione AI dipende sempre pi\u00f9 da banda e capacit\u00e0 della memoria, non solo dal numero di GPU.<\/li><\/ul>\n        <div class=\"rh-quickfacts-grid\">\n      <div class=\"rh-quickfacts-card\">\n        <div class=\"rh-quickfacts-label\">Ruolo storico della GPU<\/div>\n        <div class=\"rh-quickfacts-value\">Centrale soprattutto nella fase di training<\/div>\n      <\/div>\n    \n      <div class=\"rh-quickfacts-card\">\n        <div class=\"rh-quickfacts-label\">Nuovo fattore critico<\/div>\n        <div class=\"rh-quickfacts-value\">Efficienza del sottosistema di memoria<\/div>\n      <\/div>\n    <\/div>\n        <div class=\"fbp5-highlight\">Il messaggio chiave \u00e8 che nell\u2019AI moderna la velocit\u00e0 con cui si alimenta la GPU pu\u00f2 contare pi\u00f9 della GPU stessa.<\/div>\n        <div class=\"fbp5-info\">Kim Jeong-ho lavora da anni su HBM e packaging 2.5D e 3D, e ha collaborato allo sviluppo iniziale di HBM1 con SK hynix.<\/div>\n        \n      <div class=\"fbp5-scheda\">\n        <div class=\"fbp5-scheda-header\">Dati chiave della visione proposta<\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">Figura citata<\/div>\n            <div class=\"fbp5-scheda-val\">Kim Jeong-ho, professore al KAIST<\/div>\n          <\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">Ambito<\/div>\n            <div class=\"fbp5-scheda-val\">AI inference e architetture di memoria<\/div>\n          <\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">Memoria attuale di riferimento<\/div>\n            <div class=\"fbp5-scheda-val\">HBM, memoria ad alta larghezza di banda<\/div>\n          <\/div>\n        \n      <\/div>\n    \n      <\/section>\n    \n\n      <section class=\"fbp5-section\">\n        <div class=\"fbp5-section-label\">Tecnologie<\/div>\n        <h2>Da HBM a HBF e HBS: come pu\u00f2 cambiare la gerarchia della <span>memoria per l\u2019AI<\/span><\/h2>\n        <p>L\u2019HBM resta oggi la soluzione di memoria pi\u00f9 strettamente associata ai chip AI, ma Kim ritiene che non sar\u00e0 l\u2019unica risposta nel medio termine. Con la crescita di modelli multimodali e di sistemi agentici, aumenter\u00e0 il fabbisogno di archiviare grandi quantit\u00e0 di dati meno caldi, come video, documenti e memoria di lungo periodo.<\/p><p>Per questo entra in gioco il concetto di <strong>HBF<\/strong>, ci\u00f2\u00e8 memoria flash NAND impilata con una logica simile a quella dell\u2019HBM. L\u2019obiettivo \u00e8 offrire una riserva molto ampia di dati accessibili con maggiore efficienza rispetto alle soluzioni tradizionali, cos\u00ec da sostenere carichi AI che devono gestire informazioni eterogenee e persistenti.<\/p><p>La previsione \u00e8 ambiziosa: entro dieci anni la domanda di HBF potrebbe superare quella di HBM. Guardando ancora pi\u00f9 avanti, Kim immagina poi l\u2019arrivo di HBS, una memoria ad alta banda basata su SRAM, con velocit\u00e0 di lettura e scrittura molto superiori alla DRAM e con un ruolo da cache ultraveloce nell\u2019infrastruttura AI.<\/p>\n        <ul class=\"fbp5-bullet-list\"><li>HBF: memoria flash NAND impilata pensata per grandi volumi di dati \u201cfreddi\u201d.<\/li><li>HBS: soluzione futura basata su <strong>SRAM<\/strong>, indicata come molto pi\u00f9 rapida della DRAM.<\/li><li>Previsione di mercato: in 10 anni la domanda HBF potrebbe superare quella HBM.<\/li><\/ul>\n        <div class=\"rh-quickfacts-grid\">\n      <div class=\"rh-quickfacts-card\">\n        <div class=\"rh-quickfacts-label\">Driver tecnologico<\/div>\n        <div class=\"rh-quickfacts-value\">AI multimodale e sistemi agentici<\/div>\n      <\/div>\n    <\/div>\n        <div class=\"fbp5-highlight\">L\u2019idea non \u00e8 sostituire semplicemente la GPU, ma costruire una gerarchia di memoria pi\u00f9 adatta ai carichi AI reali.<\/div>\n        <div class=\"fbp5-info\">Nel modello descritto, le diverse memorie lavorano insieme per rifornire la GPU all\u2019interno di una struttura tridimensionale molto pi\u00f9 complessa di quella attuale.<\/div>\n        \n      <div class=\"fbp5-scheda\">\n        <div class=\"fbp5-scheda-header\">Gerarchia di memoria immaginata<\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">HBM<\/div>\n            <div class=\"fbp5-scheda-val\">Livello ad alta banda per alimentare il calcolo AI<\/div>\n          <\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">HBF<\/div>\n            <div class=\"fbp5-scheda-val\">Strato ad alta capacit\u00e0 basato su NAND flash<\/div>\n          <\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">HBS<\/div>\n            <div class=\"fbp5-scheda-val\">Cache ad altissima velocit\u00e0 basata su SRAM<\/div>\n          <\/div>\n        \n      <\/div>\n    \n      <\/section>\n    \n\n      <section class=\"fbp5-section\">\n        <div class=\"fbp5-section-label\">Prospettive<\/div>\n        <h2>L\u2019architettura AI del futuro pu\u00f2 diventare un sistema 3D a <span>circa 100 strati<\/span><\/h2>\n        <p>La parte pi\u00f9 interessante della visione di Kim \u00e8 la descrizione del computer AI del futuro come un grande edificio tridimensionale. In questa metafora, HBM, HBF e HBS avrebbero ruoli diversi ma complementari: memoria ad alta banda per il lavoro immediato, memoria ad alta capacit\u00e0 per i dati persistenti e cache ultraveloce per ridurre al minimo le attese.<\/p><p>Il risultato sarebbe una struttura composita a circa <strong>100 strati<\/strong>, con pi\u00f9 livelli di memoria impilati e coordinati per nutrire in modo continuo la GPU. \u00c8 una prospettiva che va oltre il semplice aumento della potenza dei singoli chip e punta invece a migliorare l\u2019intero flusso dei dati, che oggi rappresenta il vero limite prestazionale in molti scenari AI.<\/p><p>Per chi segue il settore dei semiconduttori, questo ragionamento \u00e8 rilevante perch\u00e9 suggerisce dove si concentreranno i prossimi investimenti: non soltanto su acceleratori sempre pi\u00f9 potenti, ma su packaging avanzato, memorie specializzate e integrazione 3D. In pratica, il vantaggio competitivo potrebbe spostarsi sempre pi\u00f9 vicino alla memoria.<\/p>\n        <ul class=\"fbp5-bullet-list\"><li>Architettura prevista: struttura 3D con circa <strong>100 strati<\/strong>.<\/li><li>Obiettivo principale: ridurre i tempi morti dovuti al movimento dei dati.<\/li><li>Aree chiave da monitorare: memoria specializzata, packaging 2.5D\/3D e integrazione di sistema.<\/li><\/ul>\n        <div class=\"rh-quickfacts-grid\">\n      <div class=\"rh-quickfacts-card\">\n        <div class=\"rh-quickfacts-label\">Capacit\u00e0 ipotizzata per HBS<\/div>\n        <div class=\"rh-quickfacts-value\">Circa 1600 GB nella visione a lungo termine<\/div>\n      <\/div>\n    \n      <div class=\"rh-quickfacts-card\">\n        <div class=\"rh-quickfacts-label\">Base fisica citata per HBS<\/div>\n        <div class=\"rh-quickfacts-value\">SRAM distribuita su un wafer da 12 pollici<\/div>\n      <\/div>\n    <\/div>\n        <div class=\"fbp5-highlight\">Se questa traiettoria si confermer\u00e0, la memoria diventer\u00e0 uno dei veri indicatori di valore delle future piattaforme AI.<\/div>\n        <div class=\"fbp5-info\">Kim ha partecipato anche alla pianificazione della roadmap di lungo periodo da HBM4 a HBM8, un dettaglio che rafforza il peso tecnico delle sue previsioni.<\/div>\n        \n      <div class=\"fbp5-scheda\">\n        <div class=\"fbp5-scheda-header\">Elementi della visione futura<\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">Struttura<\/div>\n            <div class=\"fbp5-scheda-val\">Architettura 3D composita<\/div>\n          <\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">Funzione HBS<\/div>\n            <div class=\"fbp5-scheda-val\">Livello cache ad altissima velocit\u00e0<\/div>\n          <\/div>\n        \n          <div class=\"fbp5-scheda-row\">\n            <div class=\"fbp5-scheda-key\">Direzione del settore<\/div>\n            <div class=\"fbp5-scheda-val\">Pi\u00f9 integrazione tra calcolo e memoria<\/div>\n          <\/div>\n        \n      <\/div>\n    \n      <\/section>\n    \n<section class=\"fbp5-section\" data-rh-related-section=\"1\">\n      <div class=\"fbp5-section-label\">Articoli correlati<\/div>\n      <h2>Altre notizie <span>da leggere<\/span><\/h2>\n      <div class=\"fbp5-related-grid\"><a class=\"fbp5-related-card\" href=\"https:\/\/www.chinasmartbuy.com\/blog\/aito-m9-300000-consegne-nuova-generazione-10000\/\" target=\"_blank\" rel=\"noopener\">\n      <img decoding=\"async\" src=\"https:\/\/www.chinasmartbuy.com\/blog\/wp-content\/uploads\/2026\/07\/aito-m9-supera-300-000-consegne-abcdf513-3283-4d45-8287-ab2c-5dcjbwb54f.webp\" alt=\"Aito M9 supera 300.000 consegne: la nuova generazione \u00e8 gi\u00e0 oltre 10.000 unit\u00e0\" loading=\"lazy\">\n      <div class=\"fbp5-related-body\">\n        <span>auto<\/span>\n        <strong>Aito M9 supera 300.000 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target=\"_blank\" rel=\"noopener\" title=\"Via: VIA\" aria-label=\"Via: VIA\" style=\"display:inline-flex;align-items:center;gap:8px;padding:10px 16px;border-radius:16px;background:linear-gradient(135deg,#fff7ed,#ffedd5);color:#c2410c;text-decoration:none;font-size:11px;font-weight:900;letter-spacing:.08em;text-transform:uppercase;border:1px solid rgba(249,115,22,.20);box-shadow:0 8px 22px rgba(249,115,22,.08)\">Via<\/a><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Kim Jeong-ho vede nella memoria il vero collo di bottiglia dell\u2019AI: nell\u2019inferenza la GPU lavorerebbe davvero solo per il 10%-30% del tempo.<\/p>\n","protected":false},"author":5,"featured_media":36371,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_yoast_wpseo_focuskw_text_input":"memoria AI HBM HBF 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