{"id":166100,"date":"2025-06-23T12:38:58","date_gmt":"2025-06-23T19:38:58","guid":{"rendered":"https:\/\/blog.everpuredata.com\/?p=166100"},"modified":"2025-11-10T12:54:58","modified_gmt":"2025-11-10T20:54:58","slug":"pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads","status":"publish","type":"post","link":"https:\/\/blog.everpuredata.com\/nl\/purely-technical\/pure-storage-optimizing-gpu-performance-utilization-for-ai-workloads\/","title":{"rendered":"Hoe Pure Storage computerknelpunten elimineert en GPU-gebruik voor AI-workloads optimaliseert"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column has-medium-font-size is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:70%\">\n<div class=\"wp-block-group has-border-color has-pure-orange-100-border-color has-primary-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"border-width:2px;border-top-left-radius:16px;border-top-right-radius:16px;border-bottom-left-radius:16px;border-bottom-right-radius:16px;padding-top:30px;padding-right:30px;padding-bottom:30px;padding-left:30px\">\n<h3 class=\"wp-block-heading\" id=\"h-summary\">Samenvatting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Het Pure Storage-platform pakt de technische uitdagingen van moderne AI-workloads aan, waardoor organisaties het potentieel van hun AI-infrastructuur kunnen maximaliseren.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-group pdf-print-hide is-content-justification-right is-nowrap is-layout-flex wp-container-core-group-is-layout-f726d978 wp-block-group-is-layout-flex\"><div class=\"pdfprnt-buttons\"><a href=\"https:\/\/blog.everpuredata.com\/nl\/wp-json\/wp\/v2\/posts\/166100?print=pdf\" class=\"pdfprnt-button pdfprnt-button-pdf\" target=\"_blank\" ><img decoding=\"async\" src=\"https:\/\/blog.everpuredata.com\/wp-content\/plugins\/pdf-print-pro\/images\/pdf.png?1953174090\" alt=\"image_pdf\" title=\"View PDF\" \/><\/a><a href=\"https:\/\/blog.everpuredata.com\/nl\/wp-json\/wp\/v2\/posts\/166100?print=print\" class=\"pdfprnt-button pdfprnt-button-print\" target=\"_blank\" ><img decoding=\"async\" src=\"https:\/\/blog.everpuredata.com\/wp-content\/plugins\/pdf-print-pro\/images\/print.png?245231721\" alt=\"image_print\" title=\"Print Content\" \/><\/a><\/div>\n<\/div>\n\n\n\n<div id=\"CONTENT\" class=\"wp-block-group is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Stelt u zich eens voor dat een onderneming zojuist een investering van $ 100.000 &#8211; of zelfs $ 1 miljoen &#8211; heeft gedaan in een GPU-cluster voor AI, maar slechts 62% van die GPU&#8217;s wordt consequent gebruikt bij capaciteit. Dat kan leiden tot aanzienlijke financi\u00eble verspilling en verlies van ROI.\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Maar infrastructuureigenaren kunnen een cruciale beslissing nemen om deze verliezen te voorkomen &#8211; niet alleen financi\u00eble verliezen, maar ook verloren prestaties, effici\u00ebntie en kansen. Het begint met te kijken naar een onderpresterende infrastructuur voor dataopslag, die een grote invloed kan hebben op de GPU-prestaties en GPUGPUverspillingscycli.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In AI-omgevingen is het maximaliseren van het GPU-gebruik cruciaal voor effici\u00ebnte activiteiten. Pure Storage pakt deze uitdagingen aan door opslagarchitecturen te bieden die zijn ontworpen om het GPUGPUgebruik te optimaliseren. Laten we eens kijken hoe.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technical-constraints-and-solutions\"><strong>Technische beperkingen en oplossingen<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Het <a href=\"https:\/\/www.purestorage.com\/pure-advantage\/platform.html\" target=\"_blank\" rel=\"noreferrer noopener\">Pure Storage-platform<\/a> pakt drie belangrijke technische beperkingen aan:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>latency data-inname:<\/strong> De I\/O-wachttijden verkorten om een continue datastroom te garanderen<\/li>\n\n\n\n<li><strong>Concurrency-limieten:<\/strong> Verbetering van multi-GPU-trainingsmogelijkheden<\/li>\n\n\n\n<li><strong>Doorvoervariabiliteit: <\/strong>Beheer van gevolgtrekkingen voor consistente prestaties<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-gpu-storage-interdependence-in-ai-pipelines\"><strong>GPU-opslag Onderlinge afhankelijkheid in AI-pipelines<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Moderne AI-workloads vereisen parallelle datalevering die overeenkomt met de GPUGPUgeheugenbandbreedte. NVIDIA Blackwell-GPU&#8217;s vereisen bijvoorbeeld een hoge geaggregeerde geheugenbandbreedte. Pure Storage<sup>\u00ae<\/sup> <a href=\"https:\/\/www.purestorage.com\/products\/unstructured-data-storage\/flashblade-s.html\" target=\"_blank\" rel=\"noreferrer noopener\">FlashBlade\/\/S<\/a>\u2122 levert hoge prestaties door:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>NVMe-oF-protocoloptimalisatie:<\/strong> Verbetering van de effici\u00ebntie van dataoverdracht<\/li>\n\n\n\n<li><strong>ARM-gebaseerde DirectFlash<\/strong><strong><sup>\u00ae<\/sup><\/strong>&#8211;<strong>modules:<\/strong> Overhead softwarestack verminderen<\/li>\n\n\n\n<li><strong>Dynamische pariteitstuning:<\/strong> Optimaliseren van gemengde lees-\/schrijfworkloads<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Deze architectuur reduceert data-storagecycli aanzienlijk, waardoor GPU-tensorcores verzadigd blijven.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-technical-benchmark-storage-impact-on-training-efficiency\"><strong>Technische benchmark: Impact van opslag op trainingseffici\u00ebntie<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Metrisch<\/strong><\/td><td><strong>Traditionele HDD-opslag<\/strong><\/td><td><strong>Pure Storage All-flash-oplossingen<\/strong><\/td><td><strong>Impact op training<\/strong><\/td><\/tr><tr><td>Epoch-tijd<\/td><td>3-5x langer<\/td><td>Baseline (1x)<\/td><td>Flashopslag kan de trainingstijd met 50-70% verkorten in vergelijking met HDD&#8217;s<\/td><\/tr><tr><td>GPU-gebruik<\/td><td>30-60%<\/td><td>85-98%<\/td><td>Hoger gebruik betekent dat GPU&#8217;s minder tijd kwijt zijn aan het wachten op data<\/td><\/tr><tr><td>Energie-effici\u00ebntie (FLOPS\/watt)<\/td><td>Lager<\/td><td>2-3x hoger<\/td><td>All-flash-oplossingen maken meer computing per watt vermogen mogelijk<\/td><\/tr><tr><td>Lees Latentie<\/td><td>5-10 ms<\/td><td>0,2-1 ms<\/td><td>Lagere latency zorgt ervoor dat GPU&#8217;s snel data krijgen<\/td><\/tr><tr><td>Verwerkingscapaciteit<\/td><td>100-200 MB\/s per schijf<\/td><td>5-20 GB\/s<\/td><td>Een hogere verwerkingscapaciteit voorkomt dat data uithongeren<\/td><\/tr><tr><td>IOPS<\/td><td>100-200 per schijf<\/td><td>100.000+<\/td><td>Cruciaal voor willekeurige toegangspatronen in grote datasets<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-solving-next-gen-ai-workload-challenges\"><strong>Next-gen AI-workloaduitdagingen oplossen<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Qua GPU-gebruik biedt het Pure Storage-platform:<\/p>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-129aec9c59e78960cbea3acb863ad032\" id=\"h-retrieval-augmented-generation-rag-optimization\"><strong>Retrieval-augmented Generation (RAG)-optimalisatie<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Een gezamenlijke RAG-oplossing van Pure Storage en NVIDIA omvat:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>GPU directe opslag: <\/strong>CPU-knelpunten omzeilen<\/li>\n\n\n\n<li><strong>Metadata-ge\u00efndexeerde pijpleidingen: <\/strong>Vermindering van LLM prompt latency<\/li>\n\n\n\n<li><strong>QoS-gestuurde verwerkingscapaciteit: <\/strong>Zorgen voor duurzame prestaties<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><a href=\"https:\/\/blog.everpuredata.com\/perspectives\/optimize-genai-apps-with-rag-from-pure-storage-and-nvidia\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Meer informatie over de RAG-oplossing<\/em><\/a><em>.<\/em><\/p>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-f845edf146dcfdcc6335a09576d1f724\" id=\"h-energy-efficient-scaling\"><strong>Energie-effici\u00ebnt schalen<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hardware-versnelde compressie: <\/strong>Data footprint verkleinen<\/li>\n\n\n\n<li><strong>Voorspellende tiering: <\/strong>Verhuizen van koude data naar dichtere opslag<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-2178fdc3b38ce4e79c5142b6eb3c0e06\" id=\"h-distributed-training-acceleration\"><strong>Versnelling van gedistribueerde training<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Het Pure Storage-platform biedt:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Lage leeslatentie: <\/strong>Over geo-gedistribueerde GPU-clusters<\/li>\n\n\n\n<li><strong>Geen herbouw downtime: <\/strong>Tijdens capaciteitsuitbreiding<\/li>\n\n\n\n<li><strong>Hoge cache hit rate: <\/strong>Voor multimodale datasets<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading has-secondary-color has-text-color has-link-color wp-elements-8466abb5e56b85349d79dcd031dddab3\" id=\"h-the-pure-storage-competitive-differentiation\"><strong>De Pure Storage competitieve differentiatie<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Flash-geoptimaliseerde Linux kernel stack:<\/strong> Lager CPU-gebruik<\/li>\n\n\n\n<li><strong>Dynamische RAID-geometrie:<\/strong> Behoud van een hoge uptime tijdens innamepieken<\/li>\n\n\n\n<li><strong>AI workload orkestratie API: <\/strong>Automatisering van dataplaatsing op basis van GPU-clustertopologie<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Door opslag als GPU-coprocessor te behandelen, stelt Pure Storage ondernemingen in staat om het potentieel van hun AI-infrastructuur te maximaliseren.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-implementation-guidelines\"><strong>Implementatierichtlijnen<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Voor het uitlijnen van GPU- en opslagprestaties kunt u het volgende Python-voorbeeld overwegen:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-multi-agent-rag-frameworks\"><strong>Multi-agent RAG Frameworks<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">De komst van LLM&#8217;s heeft geleid tot de ontwikkeling van geavanceerde paradigma&#8217;s zoals AI-agenten en multi-agent RAG-systemen. In tegenstelling tot conventionele RAG-pipelines &#8211; die een single-pass retrieval uitvoeren uit een solitaire externe kennisbron &#8211; orkestreren multi-agent RAG-frameworks het ophalen over meerdere gespecialiseerde agenten, waarbij elk toegang heeft tot verschillende databronnen. Deze architectuur verhoogt de complexiteit en opslag-I\/O-vereisten van het laden en controleren van data aanzienlijk om de huidige modelstatus tijdens de training op te slaan en te herstellen.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">De prestaties van het laden van data worden be\u00efnvloed door verschillende factoren op laag niveau:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Laden van de samenstelling van de pijpleiding:<\/strong> Betreft sequenti\u00eble of parallelle uitvoering van storage I\/O-bewerkingen en data-voorverwerkings-\/transformatiefasen<\/li>\n\n\n\n<li><strong>I\/O-toegangspatronen: <\/strong>Bepaald door datasetstructuur, samplingstrategie en modelspecifieke inputvereisten (bijv. sequenti\u00eble vs. willekeurige toegang)<\/li>\n\n\n\n<li><strong>Kenmerken van het opslagsubsysteem:<\/strong> Moet leesbewerkingen met een hoge verwerkingscapaciteit en lage latency ondersteunen om GPU GPU-tijd als gevolg van I\/O-knelpunten te minimaliseren<br\/><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Het controleren van prestaties wordt be\u00efnvloed door de volgende factoren:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Effici\u00ebnte dataverwerking: <\/strong>Controle in grootschalige modeltraining vereist een hoge lees- en schrijfbandbreedte om trainingsonderbrekingen tijdens opslag- en herstelactiviteiten te minimaliseren.<\/li>\n\n\n\n<li><strong>Check-pointing files: <\/strong>Controlepunten bestaan meestal uit een of meer bestanden, waarbij elk bestand wordt geschreven door een speciaal proces of thread, volgens een single-writer model om consistentie te garanderen.<\/li>\n\n\n\n<li><strong>Hoge opslagoverhead: <\/strong>Voor grote modellen en langdurige trainingstaken kunnen de totale opslagvereisten voor periodieke controlepunten aanzienlijk zijn, waardoor geoptimaliseerde opslagoplossingen en I\/O-planning nodig zijn om write amplificatie en flashopslaggebruik effectief te beheren.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Belangrijke parameters die de effici\u00ebntie van storage I\/O be\u00efnvloeden zijn onder meer sample- en batchgroottes, gelijktijdigheid (aantal reader- en writer-threads), I\/O-protocol en parallellismestrategie, asynchrone leesbewerkingen en effectiviteit van cachinglagen. Het optimaliseren van deze componenten is van cruciaal belang voor het behoud van het GPU-gebruik en het garanderen van schaalbare trainingsprestaties in multi-agent RAG-systemen.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><em>Ga voor meer informatie over het optimaliseren van AI-pipelines met Pure Storage <a href=\"https:\/\/www.purestorage.com\/solutions\/ai.html\" target=\"_blank\" rel=\"noreferrer noopener\">naar onze AIAIoplossingenpagina<\/a>.<\/em><\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><em>Lees meer over <a href=\"https:\/\/www.purestorage.com\/partners\/technology-alliance-partners\/nvidia.html\" target=\"_blank\" rel=\"noreferrer noopener\">onze samenwerking met NVIDIA<\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:30%\">\n<div class=\"wp-block-group sticky-content has-mint-green-500-background-color has-background is-layout-flow wp-block-group-is-layout-flow\" style=\"border-radius:20px;padding-top:20px;padding-right:20px;padding-bottom:20px;padding-left:20px\">\n<h2 class=\"wp-block-heading has-text-align-center has-ash-gray-500-color has-text-color has-link-color wp-elements-6cba6dc0a49af9d240e1e0430b1319a5\" id=\"h-title\" style=\"font-size:px\">Zorg voor AIAIsucces<\/h2>\n\n\n\n<p class=\"has-text-align-center has-ash-gray-500-color has-text-color has-link-color wp-elements-612f4ebc85a08a18babb164dc60dfbaa wp-block-paragraph\" style=\"font-size:clamp(0.875rem, 0.875rem + ((1vw - 0.2rem) * 0.208), 1rem);\">Lees meer over &#8217;s werelds krachtigste dataopslagplatform voor AI.\u00a0<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-f8bdad00 wp-block-buttons-is-layout-flex\" style=\"margin-top:1em;margin-bottom:1em\">\n<div class=\"wp-block-button is-style-outline button-sticky is-style-outline--2\"><a class=\"wp-block-button__link has-cloud-white-500-color has-basil-green-500-background-color has-text-color has-background has-link-color has-inter-font-family has-custom-font-size wp-element-button\" href=\"https:\/\/www.purestorage.com\/solutions\/ai.html\" style=\"border-style:none;border-width:0px;border-radius:4px;padding-top:14px;padding-right:16px;padding-bottom:14px;padding-left:16px;font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.208), 16px);\" target=\"_blank\" rel=\"noreferrer noopener\">Explore the AI Solutions Page<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Als het gaat om GPU&#8217;s, hoe vertaalt u infrastructuurtelemetrie (latentiedrempels, wattageratio&#8217;s, gebruikspercentages) in boardroom-ready waardeproposities?<\/p>\n","protected":false},"author":456,"featured_media":165796,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[14635],"tags":[14523,14843],"content-position":[],"ppma_author":[14189],"class_list":["post-166100","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-purely-technical","tag-ai-and-machine-learning-nl","tag-flashblade-s-nl"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Ontgrendel volledige GPU-prestaties in AI-pipelines met FlashBlade\/\/S | Everpure Blog<\/title>\n<meta name=\"description\" content=\"Hoe vertaalt u latency-drempels, wattage ratio&#039;s, prestaties in boardroom-ready waardeproposities als het gaat om een GPU?\" \/>\n<meta name=\"robots\" content=\"index, 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