{"id":494,"date":"2026-04-08T12:44:02","date_gmt":"2026-04-08T12:44:02","guid":{"rendered":"https:\/\/server.ua\/en\/blog\/?p=494"},"modified":"2026-04-08T12:44:02","modified_gmt":"2026-04-08T12:44:02","slug":"memory-stops-being-the-main-problem-for-ai-models","status":"publish","type":"post","link":"https:\/\/server.ua\/en\/blog\/memory-stops-being-the-main-problem-for-ai-models","title":{"rendered":"Memory stops being the main problem for AI models"},"content":{"rendered":"\n
\"Artificial
Dependence on large amounts of memory is gradually decreasing<\/figcaption><\/figure>\n\n\n\n

Until recently, running large language models was a process with a clear ceiling \u2013 the amount of available memory. If RAM was insufficient, the system would either refuse to start or run so slowly that it lost any practical meaning. This formed a persistent belief that the development of artificial intelligence depends solely on purchasing new batches of powerful GPUs. However, the engineering focus is now shifting toward algorithm efficiency rather than scaling up hardware.<\/p>\n\n\n\n\n\n\n\n

Why memory became the bottleneck<\/h2>\n\n\n\n

The issue lies in how models process requests. They do not read text instantly, but move through it step by step, storing intermediate data in the so-called KV cache. It is a kind of internal notebook where the model records the results of already processed fragments so it does not have to recompute them each time.<\/p>\n\n\n\n

This cache consumes the lion\u2019s share of GPU resources. The longer the dialogue or the larger the document, the faster the memory fills up. As a result, even top-tier graphics processors hit the limit not because of the complexity of mathematical operations, but simply due to the physical inability to fit the entire context.<\/p>\n\n\n\n

A solution from Google Research<\/h2>\n\n\n\n

The TurboQuant algorithm proposed by Google researchers changes the approach to data compression. Quantization itself is not new, but here it reaches radical levels. The method allows the KV cache to be \u201cpacked\u201d down to about three bits per value. In practice, this means memory consumption is reduced by roughly six times.<\/p>\n\n\n\n

The main advantage for engineers is that TurboQuant can be applied to already trained models. There is no need to spend weeks on additional training or large budgets on compute \u2013 the optimization is introduced directly into the working process.<\/p>\n\n\n\n

Speed and context stability<\/h2>\n\n\n\n

Reducing the weight of data automatically speeds up computation. Tests on popular architectures such as Llama 3.1 or Gemma showed that the model does not start to \u201challucinate\u201d or lose the thread of the conversation even over long distances of 100 thousand tokens.<\/p>\n\n\n\n

On H100-class GPUs, the speed of the attention mechanism \u2013 responsible for focusing the model on important parts of the text \u2013 increases by eight times compared to standard settings. This is a case where saving resources does not require compromises in output quality.<\/p>\n\n\n\n

Economics of deployment and market impact<\/h2>\n\n\n\n

Inference optimization (the direct generation of responses for the user) directly affects product cost. The ability to process more requests on the same hardware makes services more stable and more accessible. For businesses, it becomes more \u0432\u044b\u0433\u043e\u0434\u043d\u043e to invest in integrating efficient algorithms than to constantly scale server racks<\/a>.<\/p>\n\n\n\n

The reaction of the financial sector was telling. After news about such technologies, shares of major memory manufacturers such as Micron, Samsung, and SK Hynix declined. Investors understand: if demand for gigabytes in the AI sector stops growing exponentially, it will change the rules of the game for the entire semiconductor industry.<\/p>\n\n\n\n

What\u2019s next<\/h2>\n\n\n\n

TurboQuant is expected to be presented in detail at the ICLR 2026 conference. It will take some time before it appears in popular libraries such as vLLM or in cloud platforms, but the direction is clear.<\/p>\n\n\n\n

The industry is moving away from brute force toward more refined engineering solutions. This opens the door for complex neural networks in places where they were previously inaccessible due to extreme infrastructure requirements. Now even small teams get a chance to run powerful solutions on relatively modest hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"

Until recently, running large language models was a process with a clear ceiling \u2013 the amount of available memory. If RAM was insufficient, the system would either refuse to start or run so slowly that it lost any practical meaning. This formed a persistent belief that the development of artificial intelligence depends solely on purchasing […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[146,174,134],"class_list":["post-494","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-ai","tag-ram","tag-technology-development"],"_links":{"self":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts\/494","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/comments?post=494"}],"version-history":[{"count":1,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts\/494\/revisions"}],"predecessor-version":[{"id":496,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/posts\/494\/revisions\/496"}],"wp:attachment":[{"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/media?parent=494"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/categories?post=494"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/server.ua\/en\/blog\/wp-json\/wp\/v2\/tags?post=494"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}