Meta’s Ye (Charlotte) Qi participated in an internship at QCon San Francisco 2024 to discuss the challenges of running an LLM at scale.
As he stated InfoQher 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 likened the current boom in artificial intelligence to an “artificial intelligence gold rush” where everyone is chasing innovation but hitting significant roadblocks. According to Qi, deploying LLMs effectively isn’t just about deploying them on existing hardware. It’s about getting every bit of performance while keeping costs under control. This, she emphasized, requires close collaboration between infrastructure and model development teams.
Making LLM fit the hardware
One of the first challenges with LLMs is their enormous resource needs – many models are simply too large for a single GPU to handle. To solve this problem, Meta uses techniques such as splitting the model between multiple GPUs using tensor parallelism and pipelines. Qi emphasized that understanding hardware limitations is critical, as a mismatch between model design and available resources can significantly limit performance.
Her advice? Be strategic. “Don’t just take your training run or your favorite frame,” she said. “Find a runtime specialized to provide inference and deeply understand your AI problem to choose the right optimizations.”
Speed and responsiveness are non-negotiable for applications that rely on real-time outputs. Qi focused on techniques such as continuous batching to keep the system running smoothly and quantization, which reduces model precision to make better use of the hardware. These modifications, she noted, can double or even quadruple performance.
When prototypes meet the real world
Bringing the 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 by using techniques such as tiered deployments, caching systems that prioritize frequently used data, and request scheduling to ensure efficiency. Qi said that consistent hashing — a method of routing requests related to the same server — was particularly beneficial for increasing cache performance.
Automation is extremely important when managing such complicated systems. Meta relies heavily on tools that monitor performance, optimize resource utilization and streamline scaling decisions, and Qi says Meta’s proprietary deployment solutions enable enterprise services to respond to changing demands while keeping costs under control.
The big picture
Scaling AI systems is more than just a technical challenge for Qi; he’s a fool She said companies should take a step back and look at the bigger picture to see what really matters. An objective perspective helps businesses focus on efforts that deliver long-term value and continuously improve systems.
Her message was clear: success with the LLM requires more than just technical expertise at the model and infrastructure level – although at the coalface these elements are paramount. It’s also about strategy, teamwork and a focus on real-world impact.
(Photo by Unsplash)
See also: Samsung boss engages Meta, Amazon and Qualcomm in strategic tech talks
Want to learn more about cybersecurity and the cloud from industry leaders? Check out the Cyber Security & Cloud Expo in Amsterdam, California and London. Explore other upcoming enterprise technology events and webinars powered by TechForge here.