51–60 of 559 posts
Articles entire thesis looks like it can be completely de-railed if one activity happened: ai infrastructure firms cease to be able to secure more capital. Is that likely? History says it's inevitable, but timeframe is an open question.
I just realized that this blog site is pretending to be malware. I opened the tab and was constantly switching between the blog and writing this HN comment (I deleted the rest of the comment after realizing it) and was wondering where the tab went and kept opening it over and over again, then I realized that it completely rewrote the tab title with NSFW content (one of the title contained the world "nudes" with a fak…
I thought it was clever. But it also seems ham-fisted, and in poor taste.
I refuse, I'll buy when I need to and can hold on for a few months if prices become insane. This means I'll spend less on hardware then what I could, if I wanted to buy max mpro or latest framework I just will not, because prices are too mad and g o for a cheaper version. whatever happens it's crazy and hope AI madness is worth it
For example, my current Thinkpad T14-gen5, was bought with 8GB ram and 256GB NVME, and then upgraded to 64GB ram and 2TB NVME, for the same price as 16G/512G would have cost at Lenovo. And then I still have the 8GB/256GB to re-use/re-sell.
I don't buy the central thesis of the article. We won't be in a supply crunch forever. However, I do believe that we're at an inflection point where DC hardware is diverging rapidly from consumer compute. Most consumers are using laptops and laptops are not keeping pace with where the frontier is in a singular compute node. Laptops are increasingly just clients for someone else's compute that you rent, or buy a time…
Open source efforts need to give up on local AI and embrace cloud compute.
We need to stop building toy models to run on RTX and instead try to compete with the hyperscalers. We need open weights models that are big and run on H200s. Those are the class of models that will be able to compete.
When the hyperscalers reach take off, we're done for. If we can stay within ~6months, we might be able to slow them down or even break them.
If there was something 80-90% as good as Opus or Seedance or Nano Banana, more of the ecosystem would switch to open source because it offers control and sovereignty. But we don't have that right now.
If we had really competitive open weights models, universities, research teams, other labs, and other companies would be able to collaboratively contribute to the effort.
Everyone in the open source world is trying to shrink these models to fit on their 3090 instead, though, and that's such a wasted effort. It's short term thinking.
An "OpenRunPod/OpenOpenRouter" + one click deploy of models just as good as Gemini will win over LMStudio and ComfyUI trying to hack a solution on your own Nvidia gaming card.
That's such a tiny segment of the market, and the tools are all horrible to use anyway. It's like we learned nothing from "The Year of Linux on Desktop 1999". Only when we realized the data center was our friend did we frame our open source effort appropriately.
I don't buy the central thesis of the article. We won't be in a supply crunch forever. However, I do believe that we're at an inflection point where DC hardware is diverging rapidly from consumer compute. Most consumers are using laptops and laptops are not keeping pace with where the frontier is in a singular compute node. Laptops are increasingly just clients for someone else's compute that you rent, or buy a time…
> I personally dropped $20k on a high end desktop - 768G of RAM, 96 cores, 96 GB Blackwell GPU - last October, before RAM prices spiked […] 768GB of RAM is insane… Meanwhile, I’ve been going back and forth for over a year about spending $10k on a MacBook Pro with 128GB. I can’t shake the feeling I’d never actually use that much, and that, long term, cloud compute is going to matter more than sinking money into a sing…
I don't know your workloads, but for me personally 64 GB is the ceiling buffer on RAM - I can run entire k8s cluster locally with that and the M5 Pro with top cores is same CPU as M5 Max. I don't need the GPU - the local AI story and OSS models are just a toy for my use-cases and I'm always going to shell out for the API/frontier capabilities. I'm even thinking of 48 config because they already have those on 8% discounts/shipped by Amazon and I never hit that even on my workstation with 64 GB.
In such a future the iPhone and android ecosystem is dead? Because a single $1k phone is a hell of a computer. So if you can still buy a phone you can still get a computer. Local AI aside these are very capable.
Earlier quoted context omitted.
The thing is, other than AI stuff, where does a non powerful computer limit you? My phone has 16gigs of ram and a terabyte of storage, laptops today are ridiculous compared to anything I studied with. I'm not arguing mind you, just trying to understand the usecases people are thinking of here.
> other than AI stuff, where does a non powerful computer limit you? Running Electron apps and browsing React-based websites, of course.
Owning hardware is great. But I get the impression that some people view owning petty hardware as some liberty panacea. You might have a DVD collection, ten external drives, three laptops, and a workstration. You may still for all intents and purposes be wholly dependent on cloud computing, say, because that it is the only practical way to run whatever AI-driven software three years from now. Edit: That’s an example.…
https://www.reddit.com/r/LocalLLaMA/comments/1s0czc4/round_2...
I don't buy the central thesis of the article. We won't be in a supply crunch forever. However, I do believe that we're at an inflection point where DC hardware is diverging rapidly from consumer compute. Most consumers are using laptops and laptops are not keeping pace with where the frontier is in a singular compute node. Laptops are increasingly just clients for someone else's compute that you rent, or buy a time…
> I personally dropped $20k on a high end desktop - 768G of RAM, 96 cores, 96 GB Blackwell GPU - last October, before RAM prices spiked […] 768GB of RAM is insane… Meanwhile, I’ve been going back and forth for over a year about spending $10k on a MacBook Pro with 128GB. I can’t shake the feeling I’d never actually use that much, and that, long term, cloud compute is going to matter more than sinking money into a sing…