781–790 of 915 posts
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Many labs use TPUs, but not exclusively. Most labs need more compute than they can get, and if there's TPU capacity, they'll adapt their systems to be able to run partially on TPUs.
Why is AMD not more popular then if labs are so flexibly with giving away CUDA?
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that's the scenario they want to prevent. they can't force the client to use ipv4, if they connect via ipv6, they will be served an accss denied.
Yes, exactly as they would now, when the access over IPv6 is entirely unavailable. With that, the customers who don't use filtering by IPv4 would be able to use IPv6. Those who do use access control by IPv4 ranges would have time to sort out their IPv6 setup, without having anything broken at the moment when IPv6 is enabled.
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If you do the math (I did), in 2 years, open source models that you can run on a future MacBook Pro will be as capable as the frontier cloud models are today. Memory bandwidth is growing rapidly, as is the die area dedicated to the neural cores. And all the while, we have the silicon getting more power efficient and increasingly dense (as it always does). These hardware improvements are coming along as the open sourc…
A Opus 4.7/Gpt5.5 class model is 5 trillion parameters[1]. To run a 8 bit quantized version of that you need roughly 5TB of RAM. Today that is around 18 NVidia B300. That's around $900,000, without including the computers to run them in. It's true that the capability of open source models is improving, but running actual frontier models on your MPB seems a way off. [1] https://x.com/elonmusk/status/204212356166685523…
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It's a very very very fancy next token predictor Yes, and unless you are prepared to rebut the argument with evidence of the supernatural, that's all there is, period. That's all we are. So tired of the thought-terminating "stochastic parrot" argument.
Do LLMs even learn? The companies that build them build new models based partly on the conversations the older models have had with people, but do they incorporate knowledge into their neural nets as they go along? Can an LLM decide, without prompting or api calls, to text someone or go read about something or do anything at all except for waiting for the next prompt? Do LLMs have any conceptual understanding of anyt…
They learned already a lot more than any of us will. Additinal to this, you have a prompt and you can teach it things in the prompt. Like if you give it examples how it should parse things, with examples in the prompt, it becomes better in doing it.
I would say yes they learn.
"Can an LLM decide" I would argue that you frame that wrong. If a LLM is the same thing as the pure language part of our brain, than the agent harness and the stuff around it, would be another part of our brain. I find it valid to use the LLM with triggers around it.
Nonetheless, we probably can also design an architecture which has a loop build in.
"Do LLMs have any conceptual understanding" Thats what a LLM has in their latent space. Basically to be able to predict the next token in such a compressed space, they 'invent' higher meaning in that space. You can ask a LLM about it actually.
Yeah for AGI we are not there yet and we do not know how it will look like.
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A Opus 4.7/Gpt5.5 class model is 5 trillion parameters[1]. To run a 8 bit quantized version of that you need roughly 5TB of RAM. Today that is around 18 NVidia B300. That's around $900,000, without including the computers to run them in. It's true that the capability of open source models is improving, but running actual frontier models on your MPB seems a way off. [1] https://x.com/elonmusk/status/204212356166685523…
People had this "why you probably can't run a GPT-4 (or even GPT-3.5) class model on your MBP anytime soon" conversation before. Today's LLMs are able pack much more capabilities into fewer parameters compared to 2023. We might still be at the very rudimentary phase of this technology there are low-hanging efficiency gains to be had left and right. These models consume many orders of magnitude more energy than a huma…
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Nobody is winning any UX prize there. Azure, AWS, GCP... they are all terrible. Back then GCP for instance used to only work reliably on chromo-based browsers. Azure has that horrible overlay UI that abuses extended real estate that just doesn't work. But azure wins most prizes for being terrible becuase, among other things, https://isolveproblems.substack.com/p/how-microsoft-vaporize... . It's not the worst provider…
Check out hetzner ui (regardless if you like their services, i know some ppl have opions or experiences lol) BUT, their cloud ux/ui is fantasties for a cloud company!
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I kind of agree with you at this point. When ChatGPT was rapidly gaining popularity I thought that they will eventually replace search (esp. for shopping), which would have given them a huge ad revenue. Maybe they could have even tried social networking e.g., to help you sort out the huge flow of information that today's social networks are and get to the important/rewarding/whatever posts. But now ChatGPT is kind of…
OpenAI is handling 15% of US traffic.