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Comment by oxfordmale | Hacker News Reader

I think you might be reading a bit too much into this.

He’s been with Meta for 11 years and is likely in a very comfortable financial position, given the substantial stock options he’s received over that time.

He also mentioned the arrival of a new child, and it’s well known that Meta's work-life balance isn’t always ideal.

On top of that, Meta, like many major tech companies, has been shifting its focus toward LLM-based AI, moving away from more traditional PyTorch use cases.

Considering all of this, it seems like a natural time for him to move on and pursue new, more exciting opportunities.

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> On top of that, Meta, like many major tech companies, has been shifting its focus toward LLM-based AI, moving away from more traditional PyTorch use cases.

This is very wrong. Meta is on the forefront of recommendation algorithms and that's all done with traditional ML models made using PyTorch.

Meta is definitely at the forefront of recommendation algorithms built. However, the leadership team likely has shifted focus to LLMs.

Some recommendations are uncanny, except that I don't want any of them in my Facebook news feed and no matter how often I select "never show me this feed again," it keeps trying.

> toward LLM-based AI, moving away from more traditional PyTorch use cases

Wait, are LLMs not built with PyTorch?

GP is likely saying that “building with AI” these days is mostly prompting pretrained models rather than training your own (using PyTorch).

Everyone is fine-tuning constantly though. Training an entire model in excess of a few billion parameters. It’s pretty much on nobody’s personal radar, you have a handful of well fundedgroups using pytorch to do that. The masses are still using pytorch, just on small training jobs.

Building AI, and building with AI.

Fine-tuning is great for known, concrete use cases where you have the data in hand already, but how much of the industry does that actually cover? Managers have hated those use cases since the beginning of the deep learning era — huge upfront cost for data collection, high latency cycles for training and validation, slow reaction speed to new requirements and conditions.

Llama and Candle are a lot more modern for these things than PyTorch/libtorch, though libtorch is still the de-facto standard.

Pytorch is still pretty dominant in cloud hosting. I’m not aware of anyone not using it (usually by way of vLLM or similar). It’s also completely dominant for training. I’m not aware of anyone using anything else.

It’s not dominant in terms of self-hosted where llama.cpp wins but there’s also not really that much self-hosting going on (at least compared with the amount of requests that hosted models are serving)

That's wrong. Llama.cpp / Candle doesn't offer anything on the table that PyTorch cannot do (design wise). What they offer is smaller deployment footprint.

What's modern about LLM is the training infrastructure and single coordinator pattern, which PyTorch just started and inferior to many internal implementations: https://pytorch.org/blog/integration-idea-monarch/

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