He got his name in the credits. The question was if he is owed anything else. The contract he created says he was not. I’m simply suggesting he might need a different contract.
That’s a fine perspective, but the whole point of law is to guarantee outcomes. The license could easily say “if you make more than $500M, you must pay me $1M”. Why is that not an acceptable solution here?
I find it odd how we frame fairness in regards to open source software. He licensed his software as MIT. It says anyone can you use it without owing the author anything. So how is it unfair?
To be clear, I think that open source maintainers deserve much more, but I don't understand why we rarely inspect the licenses as the source of the problem.
In my experience, even with basic project concepts the small models struggle to spin up greenfield stuff. There's just too many decisions to be made and they're not good at that.
Modifying existing code is way easier if you don't expect it to be smart about it. Don't say "add X feature" and let it explore the codebase and build its own understanding. Point it at the relevant files and say "the goal is to add X feature to this code, follow Y guidelines". Now you've done the hardest part of making the decisions and it just has to follow instructions while coloring within the lines.
I think it really just depends on your goals. Slow tokens per second is fine by some people if they cost a fraction of a single node setup that can run a trillion param model. If you’re actually running a small business and want to have multiple users getting a good experience in parallel then yeah I think you need a single node. At that point you can afford it I suppose.
I don’t know what the scaling for multiple strix halo boards looks like in practice. From what I understand each server has to process the model in serial. Meaning server A has 1/4 the weights and sends server B the results to process and so on. So you don’t get compute scaling just memory scaling.
I read the comment, thanks. I just disagree with your cost estimate. Even for a small business that needs high throughput they could probably do it for far less than $300k if they aren’t just blindly buying the first big nvidia setup they can.
I don’t have any particular model in mind, sorry. My data is just rough estimates based on my experience with a single node setup. You might need to opt for a 2 or 3 bit model to get the full context window. The KV cache memory consumption as well overall performance will be heavily dependent on the model’s architecture. The performance too will depend a lot on the inference server chosen and its configuration. I suspect a sub-agent running a much smaller model would be the ideal way to get the latest knowledge via web search and summarization.
I’m not trying to say that this would be a great experience or really compete with just buying a subscription to the top models. Rather I just wanted to point out that $300k is an absurd estimate for a trillion param model meant for personal use.
It depends entirely on what you want to do and think is feasible. Small models can almost certainly run on the computer that you already have. They can do good tool calling.
You can run a trillion parameter model with decent quality for far less than $300k. A cluster of 4 AMD AI Max 395+ boards with 128GB unified memory each can be had for around $15k. That would run the 4-bit quant of a trillion param model well enough for personal use. At full use the cluster would only be consuming around 400-500W of power too. That's about the same as one high end graphics card.
That's still a lot of money, but most people don't really need a trillion parameter model. If privacy is more valuable than the frontier capabilities then they could almost certainly get by with much less.
They have it, we just haven’t enabled them. The smart model with a chat box is the wrong abstraction for local. Ideally we would have it built into applications as a clear and easy to use opt-in feature. Like allowing a user to index a folder on their hard drive and then search it semantically via embeddings. You could do that on fairly low end hardware these days. Like 2GB of RAM with any processor made within the last 10 years.
Note that AA's coding index is only made up of two benchmarks: Terminal-Bench Hard and SciCode. I'm skeptical that it makes a good coding index. It ranks Gemma 4 31B above Deepseek V4 Flash. Having used both of those models for a broad variety of coding tasks I would choose Deepseek every day.
Petsitter's default tricks doesn't seem to do much for Qwen3.6, right? JSON mode could be useful I suppose, but that's not really going to make it better at writing code. Do you have any other example tricks? I'm having a hard time understanding how I would apply them.
Meh. My server can run these models for neglible power draw (like ~130W fully maxed out). That's with ~30 tok/s which isn't that bad. I do agree that they're still nowhere near as good as the frontier models though. I do lean on those when I need to get something done with better quality or at a faster speed.
I've also been using Deepseek V4 pro/flash for some work stuff and I do find them to be much closer to frontier capability. I may try running flash at home soon for very patient edits. :)
Any artifacts or blogs I can check out? I'm curious how you manage to make them all useful in parallel. I have a hard enough time getting one instance of Qwen3.6-27B being useful full time haha.
I think this is overselling their capabilities. I've used Gemma 4 and Qwen 3.6 quite a bit on my strix halo home server. They're great models and the dense variants are significantly better, but they're still very far behind the frontier. If you boot up Gemma 4 MoE and OpenCode/Pi and expect to perform anything like Claude Code or Codex you're going to be very disappointed.
My strix halo board is feeling more useful and less toylike with the recent performance gains combined from MTP, better quantization, and generalized performance improvements across the stack. For example, I can run Unsloth's Gemma4-31B 4-bit QAT model with around 30tg and 200pp. I don't find that to be too slow at all. Particularly because it's nearly full accuracy and good enough for a lot of different stuff I throw at it.
I think it also helps that I'm using my machine to do home server stuff. It excels at all of the traditional workloads. Then I can lean on the AI to help with automation here and there. I find it deeply satisfying.
The problem with this question is that it encompasses a huge spectrum of capabilities and expectations. If you can only run an 8B model and expect it to be good at vibe coding / one shotting things you're going to have a bad time.
If you're able to run a model on the scale of ~30B, you can find that with a reasonably scoped and well defined task they do very well. I've found both Gemma4-31B and Qwen3.6-27B to be the best in this range at the moment. You can swap in the MoE models for faster inference, but they are noticeably worse at most tasks. They can one-shot / vibe code tasks with small scope, but still do much better with guidance.
If you really want frontier-like capabilities, you'll probably need at least 128GB of memory and either huge compute or a lot of patience. Most people just don't have either the money or the patience to make these local models work.
The patience required for local model usage goes far beyond just waiting for tokens though. It takes a lot of effort to get things configured and working properly for your workflow and hardware.