"Just get a 64GB Mac with 1TB of storage!"

LOL - some of us have a budget

I've just made a milestone on my project, moving away from AWS (budget) to self-hosted and the local models are so much faster than in the past. Beyond LLMs, having embeddings, image, video, audio gen available is crazy.

Running locally is the bar; it's hard to make these things a service which scales.

I’m keen to understand speed here etc etc. if I bought a Mac studio with 96GB - what can I realistically run, how’s it compare to fable/opus etc and how fast is it?

Currently maxing out two Claude code accounts every x hours when working on large code migrations or setting up new iOS apps etc - most of time it’s fine but occasionally it’s mega frustrating!

This is the kind of thing that Anthropic et al should be worried about. As it becomes easier and easier to run local models, the ceiling of what they'll be able to charge will get lower and lower. Not that nobody will be willing to pay $$$$$ per month, but a lot of people are going to multiply the per-month charge by 12 or 24 and say "Could I set up a local model for less than that, and have it pay for itself within a year or two?" And if a significant portion of customers decide to buy instead of rent, the companies whose business model is entirely centered around renting will suddenly find themselves hurting for customers.

Show us the resulting code of using them! :) I want to use local models, I have the hardware for it, but while trying them out as replacements for GPT 5.5 xhigh or Opus or other SOTA models, they aren't quite ready to be replaced yet, sadly. The quality and bumps they encounter just slows down the workflow so much, even screwing up tool call syntax sometimes.

But, for smaller more well-defined workflows, or as straight "edit this part to be like this exact" edits, they seem more than enough. Still waiting for them to become mature enough to be able to replace what we have as SOTA today, I'd say it's ready to be switched over then.

Speaking of local models, DiffusionGemma (and diffusion models in general) should not be slept on for local usage! Usually the problem locally is that the LLMs aren't efficiently making use of your hardware, unless you start batching requests and run many at the same time, but that require different approaches in general. Instead, diffusion models work much faster for individual prompts, and not by a small margin either.

Today I finally finished porting diffusiongemma-26B-A4B-it support from Transformers into Candle, and together with some optimizations I now have it basically flying with ~450 tok/s (~19 it/s) in Candle during inference, instead of ~180 tok/s (~11 it/s) from HF's Transformers library. Even using vLLM with similar sized LLMs, I don't think I've ever gotten past the ~250 tok/s threshold for single prompts, exciting stuff for local models :)

The challenge I have is getting a large enough context window so tool calls work reliably, the local models easily slip into hallucinated JSON tool responses and won't trigger the tools as a result.

After having been a happy user of Qwen3.6-27B for a few weeks, due to being away from the hardware, I'm currently forced to use Claude Sonnet 4.6

It is such a downgrade. I don't understand how that's even possible. The thing has so many strongly-held opinions I did not ever ask it for, talking just way too much and generally feeling somehow dumber.

Of course, being significantly larger, it will encode more knowledge, but that doesn't help me when I hate talking to it. And all that on top of the fact that talking with it costs real money.

I wonder what it might be that makes me hate it so much. Maybe because it doesn't see itself as a tool but almost an equal? As if its opinions would have weight.

Qwen too can act like an overeager intern, but if you tell it that it is an idiot, it will drop that ego. Not so much with Claude. In my experience, anyway.

Anyway, point is: full ack on that headline.

> “if we are constrained by performance and price, what architectural tradeoffs do we need to make?” a question that so far has not really been asked in the mad token gold rush.

To be fair, I think the labs are also interested in this (e.g OpenAI parameter golf). But the incentives are tricky. When the subsidies and tokenmaxxing era ends, local models will be essential.

> I have no concrete scientific evidence of this - my own personal vibe metric of “is a model good enough” is, “do I have to double-check it against an API model”, and GPT-OSS was the first one where I started doing that a lot less often.

The good old butt dyno!

I’ve been eyeing local models more and more with Anthropic squeezing more and more on the subscriptions. A few comments on HN had me waiting until they improved more but this article makes me wonder if I should reconsider that.

I’ve been doing some pretty niche development using a game and a script extender for said game. If these models can handle that, I’d feel good about switching.

The big caveat here is that these local models require you to invest some time tweaking your harness, AGENTS.md, and skills in order to get things roughly to the level you'd expect. But something like Qwen3.6-27B with web search capabilities and a good set of skills really is impressive! Especially considering that you can go wild and not worry about token costs.

The other thing that people tend to gloss over is that you really do need to spend some $$$ on decent hardware. Yeah, you CAN run some 4-bit quant with heavily quantized cache on your 16GB card, but it's not going to be a great experience (I think this is where a lot of the "if you think it's gonna be any good, you're going to be disappointed" stuff comes from). Yes it's a lot of $$$ upfront but it's very much unknown when hardware prices are going to come back to reality. There's a lot of hopes and dreams that any minute now an H100 will be worth pennies because "that's how it's always been" w.r.t. computer hardware, but we are living in interesting times. So you can't just make the tired old assumptions that a Claude subscription over three years time will work out to be dramatically less than the value of some card three years from now. We STILL have basically anything with >=24GB VRAM appreciating in value, which is absolutely wild. What I'm saying is, the depreciation curve may very well be a lot less dramatic and fast than it used to be, going forward.

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.

You can use a frontier model to create a plan that's specific enough for a local model of a very small size to execute on. The more specific you are and compartmentalize tasks the "dumber" the local model can be.

Edit: Obviously you'll be using more tokens but this is the trade off for running a smaller model and running locally. Similar to time memory trade off but in token economics. Sorry I need more coffee

I think gemma-4-26b-a4b and Qwen3.6-35B-A3B show that there's something very interesting about a local model that does mixture-of-experts (which helps a lot with performance) and has in the order of 30 billion parameters.

These models are very capable, and use around 20-30GB of RAM while they are running.

Provided you have 64GB of RAM that leaves space for running other applications at the same time.

I really recommend Qwen3.6 27B.

Make some tests, and its 8 bit version runs at 30tok/s when using llama.cpp with MTP and run on Macbook Max M5. I have 128 GB, but but 64 GB is well enough. https://github.com/stared/benching-local-llms-on-apple-silic...

When using benchmarks, it gives more-or-less the level of SotA mid-late 2026.

Tangential but reading on mobile, the font size in the code snippets are all over the place. I actually have the same issue on my blog. Anyone knows why?

The problem here is always the cost-benefit. For $200/mo, you're receiving subsidized best of breed access. There's no model competing for that price anywhere. If a 27B param model is what you choose, show me your hardware! I would love to be wrong...

I have used local models (around 128 gb) and the big proprietary models, and while I do want local models to win, it's important we keep the expectations of local models realistic. There are many blog posts about how local models today can fully replace some of the proprietary models and in some cases its true for the much smaller proprietary models, its very clearly much more behind the larger models.

You can be far more ambiguous with your tasks with the larger proprietary models as opposed to the local models. You can achieve the similar results with local models but you need to be much more detailed in your prompt.

One of the biggest things about running these local models is that the harness matters almost just as much as the model too. Codex is optimized for GPT models, CC is optimized for Claude, Cursor has a great harness that works very well across these providers. It took me a couple of iterations of the different harnesses to find one that would work well with the smaller Qwen models to do local coding.

I have been using qwen and glm based models from last 2 years, ended up buying mutiple machines for the same. Overall i feel 24vram is a must have to get get performance (speed wise) to match hosted soln. I have 2 machines a 12gb vram one and a 24gb one. On 12gb vram i get around 50tps generation and 500tps prompt processing and on 24gb one i get 180tps generation and 3500tps prompt processing. I have different configs for different scenarios and I also use llama cpp manager manage all my configs (https://github.com/anubhavgupta/llama-cpp-manager)