Comments URL: https://news.ycombinator.com/item?id=47887383

Points: 2

# Comments: 0

[1] https://arxiv.org/pdf/2507.06378

[2] https://pieter.ai/bpe-knockout/

The main reason we want big batches is because LLM inference is not limited by the compute, but my loading every single weight out of VRAM. Just compare the number of TFLOPS of an H100 with the memory bandwidth, there's basically room for 300 FLOP per byte loaded. So that's why we want big batches: we can perform a lot of operations per parameter/weight that we load from memory. This limit is often referred to as the "roofline model".

As models become bigger, this does not scale anymore because the model weights will not fit into GPU memory anymore and you need to distribute them across GPUs or across nodes. Even with NVLink and Infiniband, these communications are slower than loading from VRAM. NVlink is still fine for tensor parallelism, but across nodes this is quite slow.

So what MoE allows is expert parallelism, where different nodes keep different experts in memory and don't need to communicate as much between nodes. This only works if there are enough nodes to keep all experts in VRAM and have enough overhead for other stuff (KV cache, other weights, etc). So naturally the possible batch size becomes quite large. And of course you want to maximize this to make sure all GPUs are actually working.

So there are small fluctuations, often between 0.2 Hz around the base frequency, but the average is very close to the theoretical 50/60 Hz. And for an alarm clock that is good enough.

However, the DO docs are on a different level and high quality. But those are also accessible if you are not a customer.

1. Not every sequence of words deserves to be cited. GPT-3 and ChatGPT are often confidently wrong about facts, why would you want to add a citation to this? When writing a paper, this needs to be fact-checked anyways, so why not add the original (actual) source?

2. It also breaks the citation graph. Imagine all papers now point to a catch-all reference from OpenAI (2023). Adding a citation is about saying where you got certain information from and this current format doesn't give enough to do that, it just points to the catch-all. With any other citation you can either look up the paper or—in the rare case of personal communication in a citation—ask the cited source directly. You can't ask chatGPT "hey, why did you say this in paper X" and expect a meaningful answer.

So I'm glad you like the Belgian fries. What I've always learned to be key is that we fry our cut potatoes twice: once at 140°C, wait for them to cool down, and once at 175-180°C. Then you add salt, that's it.

And the mayo: I don't think many people like it like that when they eat fries at home, but it's easy ¯\_(ツ)_/¯

I was using another static site generator (Hexo), but at some point I wanted to change some things and add some custom html to my posts. Since the documentation was ... well ... minimalistic on some aspects, I also spent some time looking at the source code. But at that point I was really wondering what benefit I still had from using that generator.

In the end, all a static site generator does is collecting some markdown or RST files, converting them to html and putting that into a template html file. And generating some lists (index page, RSS, ...) and some metadata for SEO. So it took me a single Saturday to make a working static site generator and now I can do anything I want without looking up documentation or source code, since it's my own dumpster fire :-)