April 29, 2024

Today marks another milestone in the evolution of large language models (LLMs). Since ChatGPT broke onto the scene last year, it seems like every tech blog, webinar, and office water cooler has been buzzing about AI. The shift from mere buzzwords to concrete infrastructure changes is palpable. As an engineer navigating this new landscape, I can’t help but reflect on some of the challenges we’ve faced.

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Last month, our team was tasked with building a robust API endpoint that integrated with Meta Llama 3. We were excited—this was going to be a game-changer, right? But as usual, reality hit hard when we started debugging. The first issue was latency. Despite all the optimizations and improvements in hardware, there still seemed to be a wall at around 200ms response time. We spent hours profiling the code, tweaking configurations, and even reaching out to Meta for insights. Turns out, it wasn’t just our implementation—it was a fundamental constraint of how these models scale.

We also ran into some finicky issues with memory leaks in the WebAssembly implementation. One day, I found myself staring at a stack trace filled with cryptic symbols like wasm_exec_env and __malloc. Debugging this was akin to trying to catch a ghost—every time you thought you had it, it would slip away into another function call. We eventually figured out that the garbage collection settings were off, causing some of our allocated memory to never be freed.

On the platform engineering side, we started implementing DORA metrics to track our progress. This was both liberating and terrifying. Liberating because now we had a clear set of goals: lead time for changes, deployment frequency, mean time to recovery (MTTR), and failure rate trends. Terrifying because some of our numbers were far from where we wanted them to be. For instance, our MTTR was still too high—sometimes we’d spend days on single-point failures that could have been fixed in a matter of hours with better monitoring tools.

Speaking of which, the FinOps pressure is real. With cloud costs sky-rocketing due to infrastructure sprawl and unnecessary services running 24/7, we had to get more granular about our spending. We started using cost centers to trace back every expense to specific projects or features. This wasn’t just for financial purposes; it was also a way to ensure that teams were making efficient use of resources.

One of the most surprising developments this month was the Equinox.space project. It’s an ambitious initiative aimed at democratizing access to space exploration and mining. The idea is bold, but implementing it on a tight budget meant we had to get creative with our tech stack. We ended up using serverless functions for data processing and storage optimizations—every byte counted.

And then there was the debate about whether LLMs could replace human engineers. Some argued that AI would soon be able to handle all sorts of tasks, from code generation to bug fixing. Others, like myself, had our doubts. While Meta’s recent paper on LLM3 did highlight some impressive capabilities, we still found ourselves doing a lot of the heavy lifting in terms of understanding context and making nuanced decisions.

In the end, it comes down to this: while AI is becoming an indispensable tool in our toolkit, there’s still no substitute for human ingenuity. Debugging that ghostly memory leak or optimizing an API endpoint for performance—these are challenges that require not just technical skill but a deep understanding of both the problem and the context.

As we continue to navigate this exciting yet complex era of AI/ML infrastructure, I find myself looking back at ChatGPT’s launch with a mix of nostalgia and determination. Nostalgia for what was possible then, and determination to build something even better now.

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That’s my take on where we are in 2024. What about you? How has the tech landscape changed since last year? Share your thoughts in the comments below!