A post on r/LocalLLaMA this morning said a Hugging Face organisation reached the trending page on farmed likes. We pulled a sample of the accounts behind its biggest model and compared them with the accounts behind a model we know is popular. One measure we tried showed nothing. The other showed everything.
China published draft export controls today covering 23 AI technologies, and the open-weight world spent the afternoon working out whether the models it depends on are about to stop appearing. The proposed size threshold answers most of that. It sits around 280 times above the image model we checked on launch day.
On Saturday we wrote that the RTX 5090 discontinuation story rested on a single leaker’s post and said we would report back either way. Three days on there is still no statement from NVIDIA, the card is still listed on its own product page, and the second leaker being cited may be the same person as the first. Meanwhile people are picking replacement cards today.
NVIDIA is reportedly steering the GB202 die to its professional cards, which would leave the RTX 5080 as the top GeForce. Every outlet carrying it traces back to a single leaker’s post from this morning. The interesting question is not whether the card dies. It is what happens to the amount of VRAM a person can buy new.
Somebody posted a photograph of a melted power socket on an RTX 4090 that had been fine for three years. They started generating video in mid August. By 20 September the machine shut down every time a render began. We cannot diagnose their card and neither can anyone else online, but the mechanism behind this failure class is documented, and it explains why a workload change can expose something a gaming load was hiding.
Micro Center has started asking RTX 5090 buyers for a government photo ID and a signed declaration naming where the card will be installed. It is not a law, it is not confirmed at every store, and the headlines are not saying either of those things. Here is what is actually documented, and what it does to the buy-or-rent decision we published last week.
Somebody on r/LocalLLaMA published their working on whether to buy or rent an eight GPU H200 server. We ran the same arithmetic for the card most people reading this would actually put in a desktop, and the answer has moved a long way in the last year. It is not the answer we expected, and we own the card.
A Mozilla analysis put the open to closed capability gap at about 4.4 months, and it went round the local AI world as good news. It is good news. It is also being read as something it does not say, because the open model doing the catching up is 2.78 trillion parameters and will never see the inside of your computer.
We took a stock Asus TUF gaming laptop with an RTX 3070 and 8 GB of video memory, and benched eight local models overnight across ten tasks. The result that surprised us: a 30 billion parameter model with two thirds of itself off the card beat a 14 billion parameter model that fit better, by nearly four times. File size does not predict speed. All 103 measured rows are free below, failures included.
Someone asked this morning which models to back up now that NVIDIA is buying Hugging Face. We told people to keep local copies a week ago, and four days ago we wrote about our own disk filling up and breaking our publishing. Both of those are true at once, so here is the version that reconciles them.