I think it's interesting that people write off open weight models because they're "a few months behind" proprietary models.

I know LLMs move at the speed of light (especially these past few quarters), but if Opus and GPT "a few months ago" were really like open weight models, then there's really no reason to not switch, especially for those who were using these models a few months ago.

Your codebase didn't change, so use the open weight model. Don't move the goalposts.

I’ve been wanting to get better acquainted with local inference but I don’t have the hardware, which has made me think about something I haven’t seen discussed, which is local collaboratives. The economics makes it seem like a group of people joining together to run good hardware and an open model might make sense, but I haven’t seen anything like this mentioned. Have I been missing it?

I think it would be pretty neat to launch a service helping people who wanted to participate in something like that locate one another.

Sure. But OpenAI is the same price. Why would I pay $18/month for z.ai when OpenAI is $20/month?

The headline says one thing, then the article text says this:

> I’m hoping it’s going to be minimal.

I have multiple subscriptions and I pay per token to try out different LLM providers through OpenRouter. I also run open weight models locally.

I just can’t agree yet. The models from Anthropic and OpenAI really are that much better than anything else. The open weight models must be universally benchmaxxed across the board because my real world experience with them is very different than what the benchmarks imply. I get downvoted a lot for speaking about my experience because I don’t think it’s the reality that people want to hear right now, but it’s true for complex work.

I do think there are a lot of easier tasks that can be handled appropriately by the open weight models in the hands of a skilled operator. If an entire job is simple enough that you wouldn’t hesitate to hand it off to a junior with a little supervision then any model will do. However for a lot of the work I do, even Opus 4.8 on Max requires a lot of attention and extra steering and review to keep it on track. Fable did, too, though to a lesser degree. When I try to use the big open weight models (hosted, because they’re not running at reasonable speeds locally at a quantization I can tolerate) it feels like I spend more time waiting while they burn tokens for output that I probably have to reject anyway, at least for the bigger tasks. I wish they were there, but that’s not the case yet.

Have you read about Opencode Go? They are great provider for open model, like GLM 5.2, Deepseek v4 Pro, Kimi 2.7 Code. You should give it shot to them :-)

Open source models are still not good enough for now, but with the current speed of one new SOTA every two months, by this time next year we will definitely have cheap open source models at least as good as Fable :)

I think the frontier will command premium for sometime just as slight better software developers were 10x's vs their peers as their architecture & development strategies and code approach compounded quickly. One less error per block of work compounds quickly.

Sure, there may be some cases and reasons for local models and industry is so large they will continue to make progress and gather economic value and users for specific use case; but frontier will command vast majority of the economic value distinct from Linux and open source where the model created better than proriatary economic incentives around development

I think once the hardware process comes down and these mini DGXs become cheaper, and by then open models still be smaller and better, there is going to be less and less reason to use the providers. CEOs are already complaining that they are costing too much. There are also large organisations like Banks which can't use external services and are already looking at internal housing. it's a good thing so the big AI companies just went IPO as once the self hosting trend kicks in they are going bust.

But, what model are you using?

and what hardware are you using?

Is it just me or is half the article missing?

I enjoyed the first part though

As someone that has pretty powerful desktop that I've been using with local open weight models, people are far exaggerating the quality of them. Some of them are now useful. They don't compare yet to the online models of ChatGPT, Claude, Gemini, etc. They are still about 18 months behind. I have accomplished useful work with them, like image classification on Gemma4, but they are much much slower, much much more expensive and they don't scale at all.

A $10,000 RTX 6000 Blackwell card will pay for 500 months of Claude or Codex, which is 40 years worth of compute. Obviously they are going to raise their prices, my prediction being to $200-500/month, but that still makes them at least years of compute and they scale very well with more traffic. Single GPUs do not, they are pegged at 100% and good luck getting it to answer multiple queries at the same time.

>There was a time not too long ago when using Linux entailed some professional risk1. First there was compatibility: you may not have been able to render a Word document or PowerPoint correctly, and you might have had to trust Open Office’s export capability to render docs the way you wanted

For a while during this era, I used to port my laptops windows installation into a virtual machine that can run on Linux. It took a bit of hacking away but I could usually do it in a day or two. Then its all Linux with the windows vm being used for the microsoft stuff.

I know open models have gotten quite good in many tasks such as coding or composition, but are there any that can access the internet and retrieve data like ChatGPT, Claude, etc can?

I do have to admit I have recently begun wishing I could pay five dollars a month for a "just answer the fucking question" plan that would give me results without the guardrails and without the constant simpering and ego-stroking. I keep finding myself going a quick evaluation of "is it faster for me to skim search results myself or to construct an elaborate narrative to make an AI give me a real answer".

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