Meta’s Ye (Charlotte) Qi took the stage at QCon San Francisco 2024 to discuss the challenges of running an LLM at scale.
As reported InformationQHer presentation focused on what it takes to manage large models in real-world systems, highlighting the obstacles posed by their size, complex hardware requirements, and challenging production environments.
She likened the current AI boom to an ‘AI gold rush’ where everyone seeks innovation but faces serious obstacles. According to Qi, deploying LLM effectively is not simply about fitting it into existing hardware. The idea is to get all the performance out of it while keeping costs under control. She emphasized that this requires close collaboration between the infrastructure and model development teams.
Making LLM suitable for hardware
One of the first challenges of an LLM is the enormous demand for resources. Many models are too large to be processed by a single GPU. To solve this problem, Meta uses techniques such as splitting the model across multiple GPUs using tensor and pipeline parallelism. Qi emphasized the importance of understanding hardware limitations, as mismatches between model design and available resources can significantly reduce performance.
Her advice? Be strategic. “Don’t simply choose a training runtime or preferred framework,” she said. “Find a runtime specialized for inference services and gain a deep understanding of your AI problem to choose the right optimizations.”
For applications that rely on real-time output, speed and responsiveness are non-negotiable. Qi emphasized technologies such as continuous batching to keep the system running smoothly, and quantization to better utilize hardware by reducing model precision. She pointed out that these adjustments can double or even quadruple performance.
When Prototypes Meet the Real World
Taking an LLM from the lab to production is really tricky. Real-world situations result in unpredictable workloads and stringent requirements for speed and reliability. Scaling isn’t just about adding GPUs; it’s about carefully balancing cost, reliability, and performance.
Meta solves these problems through techniques such as decoupled distribution, a caching system to prioritize frequently used data, and request scheduling to ensure efficiency. Qi said consistent hashing, a method of routing related requests to the same server, was particularly helpful in improving cache performance.
Automation is critical to managing these complex systems. Meta relies heavily on tools to monitor performance, optimize resource usage and simplify expansion decisions, and Qi claims that Meta’s custom deployment solutions allow the company’s services to respond to changing demand while keeping costs in check.
big picture
Scaling AI systems is more than a technical challenge for Qi. It’s a mindset. She said companies need to step back and look at the bigger picture to figure out what’s really important. An objective perspective helps companies focus their efforts on delivering long-term value and continually improving their systems.
Her message was clear. Success with an LLM requires more than technical expertise at the model and infrastructure level. At coal sites these factors are of utmost importance. It also focuses on strategy, teamwork, and real-world impact.
(Photo source: Unsplash)
See also: Samsung CEO joins strategic technology talks with Meta, Amazon and Qualcomm
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