The Ledger
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Lin Qiao predicts a 10x AI cost reduction yields 100x more applications
“If this bar can be lowered by 10 times, you can imagine there's so many more, it will be hundred times more applications enter the, this arena to create a brand new experience to end consumers and prosumers. And by that, we'll see a much bigger consumption acr…”
For AI applications, moats lie in curated data rather than user experience
“Their mode is probably not the user experience, but because it's very easy to copy. Anyone can study the product and copy. Their mode is data.”
Lin Qiao: The future of AI modeling belongs to open-source models
“The future of the future of modeling sits on open model side. And I believe that side is gonna be much more active in creating those hundreds or maybe thousands of expert models that is specialized delivering much better quality in certain domain.”
DeepSeek runs each single model replica across more than 300 GPUs
“DeepSeq actually that company itself was running and still running this model over more than 300 GPUs. So think about this deployment. One replica is 300 GPUs, and there are so many different, so many more replicas.”
Lin Qiao: AI frameworks must reconcile researcher flexibility with strict production cost and latency constraints
“For researchers, you want the flexibility. You want ease of use. You want them to just think about what's possible, right? And for production, it's a constraint problem solving. As in, you have latency budget, you have cost budget you want to scale, you want t…”
Lin Qiao: PyTorch's primary success lesson is that simplicity scales
“I think one of the biggest success we saw from the PyTorch experience is simplicity scales.”
Fireworks AI improved speculative execution hit rates from 30% to 90%
“We have seen cases improving the prediction hit from 30% to 90%, and that's huge speed.”
Fireworks AI was first to enable function calling for DeepSeek models
“We have been working on function for calling for a long time, and we are the first one to enable function calling for deep seek models.”
Over 500 DeepSeek model variants hit Hugging Face within a month
“DeepSeq for example, just within one month of releasing their new models, There are, despite DeepSeq model, extremely hard to tune and optimize, extremely hard. There are 500, more than 500 variants published on Hugging Face, optimizing for local device, optim…”
Meta historically maintained three separate AI frameworks for mobile, research, and production
“Even within Mata, there are three different flavors. One for mobile, one for research, one for production.”
Meta spent five years rebuilding PyTorch's backend for internal scale
“It took us five years. Took us five years to get the stage supporting almost all internal needs using deep learning and mass and massive scale.”
Lin Qiao: OpenAI switched completely from TensorFlow to PyTorch
“OpenAI switched to use PyTorch fully.”
Lin Qiao: Meta had hundreds of engineers building PyTorch and its infrastructure
“We have hundreds of engineers building PyTorch and infrastructure around PyTorch, but at the same time, I believe PyTorch within Meta probably has thousands of users.”