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As a tech journalist, Zul focuses on topics including cloud computing, cybersecurity, and disruptive technology in the enterprise industry. He has expertise in moderating webinars and presenting content on video, in addition to having a background in networking technology.
Meta’s 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 the current AI boom to an “AI Gold Rush,” where everyone is chasing innovation but encountering significant roadblocks. According to Qi, deploying LLMs effectively isn’t just about fitting them onto existing hardware. It’s about extracting every bit of performance while keeping costs under control. This, she emphasised, requires close collaboration between infrastructure and model development teams.
Making LLMs fit the hardware
One of the first challenges with LLMs is their enormous appetite for resources — 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.
Her advice? Be strategic. “Don’t just grab your training runtime or your favourite framework,” she said. “Find a runtime specialised for inference serving and understand your AI problem deeply to pick the right optimisations.”
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.
When prototypes meet the real world
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’t just about adding more GPUs — it involves carefully balancing cost, reliability, and performance.
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 — a method of routing-related requests to the same server — has been notably beneficial for enhancing cache performance.
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.
The big picture
Scaling AI systems is more than a technical challenge for Qi; it’s 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.
Her message was clear: succeeding with LLMs requires more than technical expertise at the model and infrastructure levels – although at the coal-face, those elements are of paramount importance. It’s also about strategy, teamwork, and focusing on real-world impact.
(Photo by Unsplash)
See also: Samsung chief engages Meta, Amazon and Qualcomm in strategic tech talks
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