I don't know about good, I use a lot of local models and they're still pretty painful to run locally

You have dense models (qwen 27b, gemma 31b) who are pretty smart, but pretty slow

You have MoE models (gemma 26b, qwen 35b, north mini code 30b) who are pretty fast, but make a lot of mistakes

You need a lot of memory to run these well, quantization makes tool calling weaker, so most run at 4 bit quants and are wondering why it kinda sucks and that's because you've essentially lobotomized the model (I recommend unsloth quants, i recommend 6bit for MoEs and 5bit for dense)

So you need a lot of compute to make the pre-fill fast, you need bandwidth to make the decode fast, you need a lot of memory to hold everything - lot of ifs

On top of that, your laptop becomes a loud hot churning machine, it's uncomfortable to work with.

So are they good? not really. Do they work? yes

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.

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 :)

I love running two models locally: qwen3.6 27B 8bit (dense) and qwen3.6 35B 4bit (MoE).

The 27B is the smarter, more reliable one - but it is slower. The 35B is faster, still very smart but below 27B, a bit less reliable. The reason is the MoE - Mixture of Experts architecture, which only activates a subset of parameters, making the model much much faster.

I run the 27B on a MacBook Pro M5 Max + 40 GPU cores + 128GB RAM (well, on this beast I can have 27B + 35B in memory at the same time with headroom for all the other stuff). But because this is a laptop, it is not possible to run local LLMs all the time - it just gets too hot and too loud.

What excites me more: I run the 35B model on a MacMini M4 with 64GB RAM. It is fast, it gets a lot of work done (e.g. it scans, extracts and classifies my emails, it watches the mailbox all the time and does work). I also use it as my private Hermes assistant ("when is the next Starship launch?", "who is playing today at the World Cup? Give me some trivia").

Next step I am planning is a RTX Pro 6000 Blackwell workstation I can put in my basement. I want to run qwen really fast, with multiple threads / prompts / agents at once. And MAYBE if the budget allows, a 2x RTX Pro 6000 setup in order to run DeepSeek v4 flash on it (to run research on it).

I support local models and enjoy playing around with them, but even for personally development it is just more viable for me to pay $200 a month to Anthropic for the latest models. It seems to me with the cost of hardware needed to run local models that, for now, it is pure hobbyist and exploratory (which is fun in its own right)

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

Looking at some benchmarks, the latest ~30B Gemma/Qwen score similar as Claude or GPT versions that were released just one year earlier. That's crazy progress. I can't imagine how it will be in a few years.

It's more than good. As of today, it's great. Those models listed in the blog are horrible compared to what you can run today, There's absolutely no reason to run those, you have Qwen3.6, Gemma4, and plenty other sized comparable models.

If you're resourceful, you can even run SOTA models. KimiK2.7, MiMo-V2.5/V2.5-Pro, MiniMax2.5/2.7/3, DeepSeekV3.1/v3.2/V4-Flash/V4Pro, GLM5.1, Step3.7-Flash, Qwen3.5-397B, Qwen3.5-122B, gpt-oss-120B

My 2c: I think the "cloud vs local" debate is (maybe) a false dichotomy. In my experience, I use a hybrid approach and I've seen a huge productivity boost from it.

The cloud-based models are fine for big and complex tasks, but the pricing is ridiculous for small stuff—like summarizing a discussion or fixing a small bug. And cloud and privacy have never been a good match.

As an example, this comment itself was written with the help of Qwen3.5-4B running locally with an extension on top of llama.cpp default web UI [1]. The extension injects my browser's context directly into the conversation, which allows me to summarize things and draft up comments quickly. Speed is pretty acceptable for the size: ~5s TTFT and ~100 t/s generation, all running on a Macbook M5.

And when I want to run bigger tasks, I don't just stick to one provider. Apart from well-known closed-weight providers like OpenAI or Anthropic, I also experiment with open-weight models like GLM-5.1, DeepSeek V4, and Qwen3.6-27B, which provide quite good results for the price.

I'd argue both have value, and I don't see why anyone needs to choose one exclusively. Anyone else doing this?

[1]: https://github.com/ngxson/llama-companion

And if you want to dial in a setting in between: I've switched to Kimi K2.6 (now K2.7) and DeepSeek through OpenRouter and Reasonix for pretty much everything, with no discernible loss of analytical quality or utility.

However, like many commenters, I don't really believe in vibe-coding, long-horizon agentic one-shot agentic coding, etc. and do not use LLMs for huge generation tasks that involve designing things end-to-end.

I also have an MBP with 128 GB of unified memory and do quite a bit of Qwen3.6-35B-A3B. No, it's not as smart as the aforementioned models, to say nothing of frontier, but many people seem pleasantly shocked by the number of banal tasks that do not require these.

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

LOL - some of us have a budget

I tried some smaller Gemma4 and Qwen3.6 quants on my MBA with M5/16GB and had like 20-60 tokens per second. At 60 it felt pretty okay and that hardware is on the lower end.

I'd assume a Mac with 32-64GB memory would get some reasonable results.

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.

If vibe-coding is hopping into a self-driving car and telling it to take you anywhere you can get a coffee, then I use coding agents more like a bicycle - they let me get further faster than if I'd walked, but I still have to decide where to go and how to get there, and I still have to pedal.

I don't vibe-code, but I do decide what to implement and what patterns to use (perhaps asking the model to analyze and give advice on this first), then I have it handle the nitty-gritty of the implementation itself. For this usage style, the latest local models are as good as having Claude at home.

I won't say it's been _easy_ (I ended up implementing my own harness to accommodate the idiosyncrasies of local models), but I will say that for the effort, having a coding agent that's essentially free to query as much as I want has been life-changing as a dev, especially when it comes to working on side projects. Knowing that my agent will never get worse in quality, suddenly cost more than it does now, or be suddenly made unavailable by external factors, was absolutely worth the trouble. And on top of all that, I can't believe it's as good as it is.

I love that local LLMs are being discussed more often on HN recently. But for the post, I find it strange that the author claimed they were working with local models from day 1, but wrote a post that still links to Qwen2.5 and Qwen3 in mid June 2026.

Good depends a lot. If you are in the token maxxing hype you will probably find these models very bad comparing to SOTA, unfortunately.

The good news might be: opensource models are now good (enough) for day2day usage. But is it really? I feel that companies will always naturally strive for the best and use the SOTA (as long it is not too expensive).

I see OSS models being a good backbone for companies in the future that have validated workflows and could use those for privacy or to spare costs.

IDK, might have gone a little bit off-topic here.

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!

I've been running Qwen3.6-35B-A3B (and 3.5 previously) locally and it's a great model for many small tasks, probably a significant chunk of what most normal people are using LLMs for right now.

But for coding in a harness? In my experience it's unusable even for small projects. It just gets hard stuck at every little problem, wasting hundreds of thousands of tokens trying to make a convoluted solution work instead of doing the obvious thing. Or it will spend hours trying to reason through a fairly simple code flow, incrementally adding debug print statements, only to get confused by the output and then editing completely unrelated code that it convinced itself is the problem.

I've tried instead giving Sonnet the problem description and code and have it come up with a detailed plan that Qwen should implement, but doing that actually consumes a significant amount of tokens compared to just telling it to implement everything, and the results are honestly not that much better. There are just too often subtle issues with the plan that Qwen doesn't recognize when implementing, but make the resulting solution it comes up with unusable.

There are good use cases for them for sure, the Gemma 4 Good hackathon a while ago showed how local models can solve problems in health and education in areas with low connectivity or small infrastructure.

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.

> “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.

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 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)

I am happy to pay OpenAI for a cheaper model a few generations behind. But they deprecate models aggressively. They push you to bigger and smarter models, when 95% of my work doesn’t need it.

I’d love it if model providers just let old models run and let us pay less, but the deprecation makes me want to look into local models.

I currently have a desktop with a 4060 ti (16gb of vram). Most models I have tested that fit within that are not good enough for anything other then type completion (in regards to coding tasks)

I have been considering getting the 58gb Mac Mini but that is a decent amount of money to spend without confirmation on a) how fast is it and b) will it work for well-defined tasks.

> 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.

I wonder how much local models hallucinate. I am getting almost daily an "Honest answers: I made that up." reply from Claude Opus when I challenge some silly thing it's trying to do.

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.

> I have a 2022 M2 Mac with 64 GB RAM

I closed the article after that.

The author has no idea what a privilege it is to have a machine like that for personal use, and how 99% of the population are not going to afford a setup like that.

Just some back-of-the-envelope maths will tell you that a $20/month Claude subscription makes much more sense financially.

Is there a local harness designed around the local model use case that is claude code like? Opencode has been problematic at times, pi works for one off for me but not back and forth conversations with the LLM. Considering I only use Qwen or Gemma models I'm close to just writing my own at this point

If you wanted to do some research or learn about post training and agent harnesses, is that a good option with these local models? What hardware is recommended, or easiest to go with a Mac Studio with 64GB+ RAM?

The problem with QWEN is that it just can't edit files reliably, I had to hack Pi all over to reduce the pain, but still far from perfect...does Gemma 4 strugle on this?

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.

With Apple silicon and now the RTX Spark there are real discussions whether local AI is the future. The only problem is Western open source models are so far behind. I genuinely feel there's a push to fix this. Gemma is getting more frequent releases and Nvdia is quietly creating very cool small models. I hope both the hardware and models catch up and local really does emerge.

In my experience, with a system of 32GB RAM and 24GB VRAM, no, they aren't that good.

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?

really want to try local models, but I don't have the hardware yet. Probably I'm the only one here still using a Mac Mini m1 8gb 2020. :/

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 2025.

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.

This is actually a really insightful post !

open source must win

https://canirun.ai

I've been trying local models for the boring stuff you might be thinking about: writing small docs.

So i've tested a couple, and the speed is finally impressive. My colleague uses paid tiers of claude and GPT, and the speed is comparable. Maybe even slightly faster on my end.

The problem is: i'm running the model on my work laptop, a 12th gen i5 with 16GB of RAM (which, you know, i asked to upgrade to 64, but that was right at the time of the great RAM shortage of the '20s) so i'm pretty limited in what i can use. And this is running alongside the usual suspects: Web browser hugging 1.5GB, MPLABX hugging 3, windows taking at least 5 just to sit idle, thermal throttled to 1GHz ... And yet its speed is comparable to a paid service. A lunch's worth of tokens vs a few cents of power.

So, what i found, what i fount... What i found is that i need AT LEAST 16k of context window, otherwise they will halt when i pass a small C file for analysis. And coding models will shit the bed with 4k. But we all know that, context size is King.

I found out that Qwen will keep looping while thinking, but that's not a surprise to you, either. But give it enough time and you will get an useful answer. I was hoping to using it as a better warning system for some languages, but i fear i need muuuch more context size, because i tried to feed a file that had a function with an endless loop:

At 4k context it almost shit the bed if i gave it just the offending function, then told it where to look at. At 16k context, with the whole file, it needed some guidance to what the problem was, and after 10-15 minutes of thinking it found the issue. Problem is, it kept second guessing itself for another 20 minutes on the same unrelated thing before giving the output. For which the fix was wrong, but the semanthic was correct. Good enough. Maybe it will be faster if i don't ask for a fix (which i didn't i just asked to look for a specific issue)

Wish i had 3 times the RAM so i can see what happens with more context.

Then i gave it the task to analyze a C file to make an API document. It took half an hour, but then i had a good starting point, which i had to keep changing because it would confuse commands with IDs and things like that.

This was the Qwen 3.5 9B model.

I then tested Gemma 4, being impressed at the tokens per second it gives on my Pixel 8A. Same tasks: same issues with short context, with long context it gave absolutely useless answers when looking at code, but it took 1/3 the time of qwen.

In producing documentation, instead, it was much faster, and it never hallucinated data. Good. in 15 minutes i had everything done.

Not bad for stuff running on a business laptop, while doing actual work.

Tomorrow i will try Qwen 3.6, let's see how it goes..