{"id":101505,"date":"2024-12-03T08:41:50","date_gmt":"2024-12-03T08:41:50","guid":{"rendered":"https:\/\/www.cloudcomputing-news.net\/?p=101505"},"modified":"2024-12-03T08:41:50","modified_gmt":"2024-12-03T08:41:50","slug":"building-the-future-of-ai-systems-at-meta","status":"publish","type":"post","link":"https:\/\/www.cloudcomputing-news.net\/news\/building-the-future-of-ai-systems-at-meta\/","title":{"rendered":"Building the future of AI systems at Meta"},"content":{"rendered":"\n

Meta\u2019s Ye (Charlotte) Qi took the stage at QCon San Francisco 2024, to discuss the challenges of running LLMs at scale.<\/p>\n\n\n\n

As reported by InfoQ<\/a><\/em>, her presentation focused on what it takes to manage massive models in real-world systems, highlighting the obstacles posed by their size, complex hardware requirements, and demanding production environments.<\/p>\n\n\n\n

She compared the current AI boom to an \u201cAI Gold Rush,\u201d where everyone is chasing innovation but encountering significant roadblocks. According to Qi, deploying LLMs effectively isn\u2019t just about fitting them onto existing hardware. It\u2019s about extracting every bit of performance while keeping costs under control. This, she emphasised, requires close collaboration between infrastructure and model development teams.<\/p>\n\n\n\n

Making LLMs fit the hardware<\/h2>\n\n\n\n

One of the first challenges with LLMs is their enormous appetite for resources \u2014 many models are simply too large for a single GPU to handle. To tackle this, Meta employs techniques like splitting the model across multiple GPUs using tensor and pipeline parallelism. Qi stressed that understanding hardware limitations is critical because mismatches between model design and available resources can significantly hinder performance.<\/p>\n\n\n\n

Her advice? Be strategic. \u201cDon\u2019t just grab your training runtime or your favourite framework,\u201d she said. \u201cFind a runtime specialised for inference serving and understand your AI problem deeply to pick the right optimisations.\u201d<\/p>\n\n\n\n

Speed and responsiveness are non-negotiable for applications relying on real-time outputs. Qi spotlighted techniques like continuous batching to keep the system running smoothly, and quantisation, which reduces model precision to make better use of hardware. These tweaks, she noted, can double or even quadruple performance.<\/p>\n\n\n\n

When prototypes meet the real world<\/h2>\n\n\n\n

Taking an LLM from the lab to production is where things get really tricky. Real-world conditions bring unpredictable workloads and stringent requirements for speed and reliability. Scaling isn\u2019t just about adding more GPUs \u2014 it involves carefully balancing cost, reliability, and performance.<\/p>\n\n\n\n

Meta addresses these issues with techniques like disaggregated deployments, caching systems that prioritise frequently used data, and request scheduling to ensure efficiency. Qi stated that consistent hashing \u2014 a method of routing-related requests to the same server \u2014 has been notably beneficial for enhancing cache performance.<\/p>\n\n\n\n

Automation is extremely important in the management of such complicated systems. Meta relies heavily on tools that monitor performance, optimise resource use, and streamline scaling decisions, and Qi claims Meta’s custom deployment solutions allow the company’s services to respond to changing demands while keeping costs in check.<\/p>\n\n\n\n

The big picture<\/h2>\n\n\n\n

Scaling AI systems is more than a technical challenge for Qi; it\u2019s a mindset. She said companies should take a step back and look at the bigger picture to figure out what really matters. An objective perspective helps businesses focus on efforts that provide long-term value, constantly refining systems.<\/p>\n\n\n\n

Her message was clear: succeeding with LLMs requires more than technical expertise at the model and infrastructure levels \u2013 although at the coal-face, those elements are of paramount importance. It\u2019s also about strategy, teamwork, and focusing on real-world impact.<\/p>\n\n\n\n

<\/p>\n\n\n\n

(Photo by Unsplash<\/a>)<\/em><\/p>\n\n\n\n

See also: Samsung chief engages Meta, Amazon and Qualcomm in strategic tech talks<\/a><\/strong><\/p>\n\n\n\n

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo<\/a> taking place in Amsterdam, California, and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here<\/a>.<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Meta\u2019s Ye (Charlotte) Qi took the stage at QCon San Francisco 2024, to discuss the challenges of running LLMs at scale. As reported by InfoQ, her presentation focused on what it takes to manage massive models in real-world systems, highlighting the obstacles posed by their size, complex hardware requirements, and demanding production environments. She compared […]<\/p>\n","protected":false},"author":59514,"featured_media":101506,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1,30,33],"tags":[172,182],"ppma_author":[1844],"class_list":["post-101505","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cloud-computing","category-cloud-computing-infrastructure","category-cloud-computing-research","tag-ai","tag-cloud"],"acf":[],"yoast_head":"\nBuilding the future of AI systems at Meta<\/title>\n<meta name=\"description\" content=\"Meta showcased its approach to scaling LLMs, revealing how advancements in AI infrastructure are addressing the demands of generative AI.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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