> Many of the best models are open source, just too big to run for most people.

You can find all of the open models hosted across different providers. You can pay per token to try them out.

I just don't see the open models as being at the same quality level as the best from Anthropic and OpenAI. They're good but in my experience they're not as good as the benchmarks would suggest.

> $10k is a lot of money, but that ability to fit these very large models & get quality results is very impressive.

This is why I only appreciate the local LLM scene from a distance.

It’s really cool that this can be done, but $10K to run lower quality models at slower speeds is a hard sell. I can rent a lot of hours on an on-demand cloud server for a lot less than that price or I can pay $20-$200/month and get great performance and good quality from Anthropic.

I think the local LLM scene is fun where it intersects with hardware I would buy anyway (MacBook Pro with a lot of RAM) but spending $10K to run open models locally is a very expensive hobby.

> Many of the best models are open source, just too big to run for most people

I don't think that's a likely future, when you consider all the big players doing enormous infrastructure projects and the money that this increasingly demands. Powerful LLMs are simply not a great open source candidate. The models are not a by-product of the bigger thing you do. They are the bigger thing. Open sourcing a LLM means you are essentially investing money to just give it away. That simply does not make a lot of sense from a business perspective. You can do that in a limited fashion for a limited time, for example when you are scaling or it's not really your core business and you just write it off as expenses, while you try to figure yet another thing out (looking at you Meta).

But with the current paradigm, one thing seems to be very clear: Building and running ever bigger LLMs is a money burning machine the likes of which we have rarely or ever seen, and operating that machine at a loss will make you run out of any amount of money really, really fast.

https://pcisig.com/pci-sig-announces-pcie-80-specification-t...

From 2003-2016, 13 years, we had PCIE 1,2,3.

2017 - PCIE 4.0

2019 - PCIE 5.0

2022 - PCIE 6.0

2025 - PCIE 7.0

2028 - PCIE 8.0

Manufacturing and vendors are having a hard time keeping up. And the PCIE 5.0 memory is.. not always the most stable.

You'll want to look at benchmarks rather than the theoretical maximum bandwidth available to the system. Apple has been using bandwidth as a marketing point but you're not always able to use that bandwidth amount depending on your workload. For example, the M1 Max has 400GB/s advertised bandwidth but the CPU and GPU combined cannot utilize all of it [1]. This means Strix Halo could actually be better for LLM inference than Apple Silicon if it achieves better bandwidth utilization.

[1] https://web.archive.org/web/20250516041637/https://www.anand...

The game changer technology that'll enable full 1TB+ LLM models for cheap is Sandisk's High Bandwidth Flash. Expect devices with that in about 3-4 years, maybe even on cellphones.

> Like, just straight up can’t do it even though they’re advertised as being able to. Most of the models I’ve tried with Goose (models which say they can do tool calls) will respond to my questions about a codebase with “I don’t have any ability to read files, sorry!”

I'm working on solving this problem in two steps. The first is a library prefilled-json, that lets small models properly fill out JSON objects. The second is a unpublished library called Ultra Small Tool Call that presents tools in a way that small models can understand, and basically walks the model through filling out the tool call with the help of prefilled-json. It'll combine a number of techniques, including tool call RAG (pulls in tool definitions using RAG) and, honestly, just not throwing entire JSON schemas at the model but instead using context engineering to keep the model focused.

IMHO the better solution for local on device workflows would be if someone trained a custom small parameter model that just determined if a tool call was needed and if so which tool.

Agreed that this is a huge limit. There's a lot of examples actually of "tool calling" but it's all bespoke code-it-yourself: very few of these systems have MCP integration.

I have a ton of respect for SGLang as a runtime. I'm hoping something can be done there. https://github.com/sgl-project/sglang/discussions/4461 . As noted in that thread, it is really great that Qwen3-Coder has a tool-parser built-in: hopefully can be some kind useful reference/start. https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct/b...

Qwen 3 Coder 30B-A3B has been pretty good for me with tool calling.

This resonates. I have finally started looking into local inference a bit more recently.

I have tried Cursor a bit, and whatever it used worked somewhat alright to generate a starting point for a feature and for a large refactor and break through writer's blocks. It was fun to see it behave similarly to my workflow by creating step-by-step plans before doing work, then searching for functions to look for locations and change stuff. I feel like one could learn structured thinking approaches from looking at these agentic AI logs. There were lots of issues with both of these tasks, though, e.g., many missed locations for the refactor and spuriously deleted or indented code, but it was a starting point and somewhat workable with git. The refactoring usage caused me to reach free token limits in only two days. Based on the usage, it used millions of tokens in minutes, only rarely less than 100K tokens per request, and therefore probably needs a similarly large context length for best performance.

I wanted to replicate this with VSCodium and Cline or Continue because I want to use it without exfiltrating all my data to megacorps as payment and use it to work on non-open-source projects, and maybe even use it offline. Having Cursor start indexing everything, including possibly private data, in the project folder as soon as it starts, left a bad taste, as useful as it is. But, I quickly ran into context length problems with Cline, and Continue does not seem to work very well. Some models did not work at all, DeepSeek was thinking for hours in loops (default temperature too high, should supposedly be <0.5). And even after getting tool use to work somewhat with qwen qwq 32B Q4, it feels like it does not have a full view of the codebase, even though it has been indexed. For one refactor request mentioning names from the project, it started by doing useless web searches. It might also be a context length issue. But larger contexts really eat up memory.

I am also contemplating a new system for local AI, but it is really hard to decide. You have the choice between fast GPU inference, e.g., RTX 5090 if you have money, or 1-2 used RTX 3090, or slow, but qualitatively better CPU / unified memory integrated GPU inference with systems such as the DGX Spark, the Framework Desktop AMD Ryzen AI Max, or the Mac Pro systems. Neither is ideal (and cheap). Although my problems with context length and low-performing agentic models seem to indicate that going for the slower but more helpful models on a large unified memory seems to be better for my use case. My use case would mostly be agentic coding. Code completion does not seem to fit me because I find it distracting, and I don't require much boilerplating.

It also feels like the GPU is wasted, and local inference might be a red herring altogether. Looking at how a batch size of 1 is one of the worst cases for GPU computation and how it would only be used in bursts, any cloud solution will be easily an order of magnitude or two more efficient because of these, if I understand this correctly. Maybe local inference will therefore never fully take off, barring even more specialized hardware or hard requirements on privacy, e.g., for companies. To solve that, it would take something like computing on encrypted data, which seems impossible.

Then again, if the batch size of 1 is indeed so bad as I think it to be, then maybe simply generate a batch of results in parallel and choose the best of the answers? Maybe this is not a thing because it would increase memory usage even more.

I dropped $4k on an (Intel) laptop a few years ago. I thought it would blow my old 2012 core i7 out of the water. Editing photos in Lightroom and Photoshop often requires heavy sustained CPU work. Thermals in laptops is just not a solved problem. People who say laptops are fine replacements for desktops probably don't realize how much and how quickly thermals limit heavy multi-core CPU workloads.

I'm a desktop guy, considering the switch to a laptop-only setup, what would I miss ?

I think this would be more interesting if you were to try to prove yourself correct first.

There are extremely few things that I cannot do on my laptop, and I have very little interest in those things. Why should I get a computer that doesn't have a screen? You do realize that, at this point of technological progress, the computer being attached to a keyboard and a screen is the only true distinguishing factor of a laptop, right?