So check this out, guys. OpenAI, the big brains behind ChatGPT, ran into a bit of trouble right after they launched this thing. Turns out, some sneaky group out there reverse-engineered their API and started abusing it. I mean, come on! These hackers found a loophole and went to town on it.
But here’s where it gets interesting. OpenAI’s Engineering Manager, Evan Morikowa, shared this hilarious story during a talk at the LeadDev West Coast 2023 event. He revealed that one of their engineers noticed some weird traffic on their endpoints that didn’t match their standard client. That’s when they knew something fishy was going on.
So, what did they do? They decided to have a little fun with it. They made their language model, the LLM, respond like a cat to every prompt. Can you imagine the confusion this caused the hackers? OpenAI even lurked in the group’s Discord just to see the chaos unfold. It was like watching a bunch of cats chasing their own tails, man. Talk about getting caught red-handed!
Now, let’s shift gears a bit and talk about the challenges OpenAI faced in scaling ChatGPT. You see, they didn’t anticipate some unexpected engineering hurdles that came with this growth. One of the big problems was a shortage of GPU and HBM3. I mean, the demand for this stuff was insane, and they were scrambling to keep up.
Morikowa explained that bottlenecks can show up anywhere in the system. It could be the memory bandwidth, network bandwidth between GPUs, or even between nodes. And the crazy thing is, the location of these bottlenecks changes depending on the size of the model and how it’s being used.
Now, get this. These guys were running NVIDIA A100 GPUs, equipped with special High Bandwidth Memory (HBM) and this NVLink interconnect that packs a serious punch. We’re talking about 200 to 400 gigabits of network power, baby!
But here’s where things get tricky. OpenAI realized that scaling the company brought on some unique challenges that nobody saw coming. In particular, caching all the calculations ChatGPT does puts some serious strain on their memory. They had to find ways to make the most out of their resources, and it wasn’t easy.
You see, they had to store this cache in this super-fast HBM3 memory bonded to the GPUs. Pushing data through a PCIe bus was slow as molasses compared to the speed of this memory. But, here’s the catch, this memory is expensive and limited. Most of it is taken up by storing the model weights.
So, OpenAI had to come up with ways to manage this cache efficiently. They would expire old data first, and if there was a cache miss, they had to recompute everything from scratch. And since they share GPU RAM across different users, your conversation could get evicted if it went idle for too long. It’s like trying to find a parking space in a crowded city, man.
But here’s the thing, GPU RAM became their most valuable commodity. It became the bottleneck, not the compute power. And let me tell you, cache misses had a huge impact on how much work those GPUs were doing. It was a balancing act, my friends.
And you know what? OpenAI wasn’t the only one facing these challenges. Chip manufacturers like Nvidia were scratching their heads too. It’s hard to get the balance just right between compute power and memory bandwidth. Morikowa pointed out that future ML architectures and sizes were unpredictable, so it was tough for them to design chips that fit perfectly.
However, through all these struggles, OpenAI learned some valuable lessons. First, they realized that treating this as a systems engineering challenge was crucial. This wasn’t just a pure research project, man. Second, adapting to the constraints of these systems was key. They had to be flexible and think on their feet. And finally, diving deep into the nitty-gritty details of the system was essential. The more they understood, the better they became.
So there you have it, folks. OpenAI had some wild adventures with ChatGPT, dealt with shortages of GPUs and memory, and faced all sorts of unexpected challenges. But hey, they came out on top, learning and evolving along the way. It’s all part of the journey, my friends.