Building the future of AI systems at Meta

Meta’s Ye (Charlotte) Qi took the stage at QCon San Francisco 2024 to discuss the challenges of running an LL.M. at scale.

It is reported InformationQIn her talk, she focused on how to manage large models in real-world systems, highlighting the obstacles posed by the model’s size, complex hardware requirements, and demanding production environments.

She likened the current AI boom to an “AI gold rush,” where everyone is chasing innovation but encounters significant obstacles. Qi said deploying the LLM effectively involves more than just installing it onto existing hardware. This is to maximize performance while controlling costs. She emphasized that this requires close collaboration between the infrastructure and model development teams.

Making the LL.M. fit for hardware

One of the first challenges faced by LL.M.s is their huge demands on resources – many models are too large for a single GPU to handle. To solve this problem, Meta employs techniques such as splitting the model across multiple GPUs using tensors and pipeline parallelism. Qi emphasizes that understanding hardware limitations is critical, as a mismatch between model design and available resources can severely impact performance.

Her advice? Be strategic. “Don’t just grab your training runtime or your favorite framework,” she says. “Find a dedicated runtime for inference services and gain insights into your AI problem to choose the right optimization.”

For applications that rely on instant output, speed and responsiveness cannot be ignored. Qi highlights techniques such as continuous batching to keep the system running smoothly, and quantization to reduce model accuracy to better utilize the hardware. She notes that these tweaks can double or even quadruple performance.

When prototype meets the real world

Moving the LLM from laboratory 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’s about carefully balancing cost, reliability, and performance.

Meta solves these problems through technologies such as decomposed deployment, a cache system that prioritizes commonly used data, and request scheduling to ensure efficiency. Qi says consistent hashing, a method of routing related requests to the same server, is very beneficial for improving caching performance.

Automation is extremely important in managing such complex systems. Meta relies heavily on tools to monitor performance, optimize resource usage and simplify scaling decisions, and Qi claims that Meta’s customized deployment solutions enable the company’s services to respond to changing needs while controlling costs.

overall view

For Qi, scaling AI systems is more than a technical challenge; it’s a mindset. Companies should take a step back, look at the big picture and figure out what’s really important, she said. An objective perspective helps businesses focus on efforts to deliver long-term value and continuously improve systems.

Her message is clear: success in the LLM requires more than technical expertise at the model and infrastructure level – although at the coalface these elements are crucial. It’s also about strategy, teamwork and focusing on real-world impact.

(photography: Not splashed)

See also: Samsung CEO holds strategic technology talks with Meta, Amazon and Qualcomm

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