Qwen 3.6 27B is the sweet spot for local development

The article is based on running Qwen 3.6 on a 128GB MacBook Pro. For reference, a 128GB MBP currently starts at $6699 USD [0]

Some people will be happy to pay that premium for privacy, but at roughly 10X the cost of a MacBook Neo, that money could also buy a lot of credits on OpenRouter or frontier labs.

[0]: https://www.apple.com/shop/buy-mac/macbook-pro/14-inch-space...

None of the examples reflect 'real work', at least not what I'd consider real work. Being able to nail a zero-shot greenfield project is relatively easy even for a small model. There's not much context to build up and it can fall back to similar examples in the training data easily. So long as you're not asking it to invent something wholly new it'll probably manage.

The real test is whether or not it can work with your existing codebases. In my limited experiments Qwen 3.5 (maybe 3.6 is loads better) does OK on a Rust+React app, and less well on a C# monolith. Not to the point of being unusable but definitely poorly enough that I went back to Claude after 20 minutes. If I lost access to a cloud model and had to use Qwen instead I'd be visibly sad.

I've been working with local models for the past year. There's so many possibilities, but I don't think coding is one. Coding requires so many layers beyond inference; I spent so much time trying to replicate what Claude Code does end to end locally. Understanding all the layers and keeping up with the advancements feels like a slog. Even this article messes up and misunderstands what some of the settings are doing. Qwen in particular seems to work at first, then often gets stuck in thought loops when used for actual work.

However, text-to-speech, speech-to-text, and non-code LLM use cases are so useful to have local, and don't require big hardware.

Having a universal reliable inference engine interface, I think, is the big unlock that needs to happen before app devs can ship these features.

Personal concrete use case: meeting recording app. This uses Parakeet + Qwen to create local transcriptions and post-cleanup, respectively.

Right now this app has to download and manage all these models, then bundle an inference engine to run them. It's a lot of code that probably should belong to the OS, or at least a standard interface.

While apps can offload some of this to llama.cpp or a similar process over http, that's another set of setup for the user to do before they can have a useful app.

Anyway, if you're getting started on a Mac, I'd suggest trying out oMLX (https://github.com/jundot/omlx) before messing with llama.cpp. In particular they have community benchmarks so you can see what kind of performance you're likely to get: https://omlx.ai/benchmarks. I wished each one had more configuration details though.

I feel like I'm going insane seeing people buy these 128gb MBP for thousands of dollars to run models that are objectively much worse than SOTA and spending so much more. The amount spent on a 128gb M5 MAX can buy you a damned new car here. What the hell am I missing? Are developers in other countries living in such different worlds?

(I'm aware the price is, in absolute terms, more expensive where I live compared to the USA. That reinforces what I think, because anyone sane that would've bought one of those in another country would sell them as soon as they landed here and save that money.)

Is there any hope for people that cant even run 27B parameters, Qwen3.6 or otherwise? Are there any quantized models that do well with tool calling at smaller parameter sizes?

I do not have a crazy rig, a modest gaming one at that, but in trying to understand more about agents and their capabilities, I am SOL with my 16 GB of RAM and 8GB of VRAM. I can get most small, non tool calling models to perform well, but I've had major issues with anything over 9B doing anything more than reasoning (egregiously slow at higher parameter counts).

And so far, I cant get even Pi to extend itself or do any meaningful work with any of the models I currently can get to run.

FWIW I'm running gemma4 31b on my 5090 and it's pretty great as well.

QAT, MTP, 128k context.

I liked Qwen 3.6 27b too, it just seems that Gemma4 is a bit underrated.

> ... on my Macbook Max M5 128 GB

Local development for who? How many of y'all are rocking 128GB of memory? Am I reading Apple's site correctly that it's a $10,000 laptop?

I'd also look at the qwopus distil if you're using qwen 3.6 27b. It's a nice refinement of the current 27b with slightly better stats.

Jackrong has a few different ones available depending on what you're trying to do: https://huggingface.co/Jackrong

Call me back when you can run these models on 16GB of RAM and any recent i5/i7. Until then, there’s no point on using these toy models.

I have been having pretty good success with Qwen 3.5 9B for "nontrivial but not challenging work all things considered" -- it runs great on my 24gb unified memory m4 pro MacBook Pro. What do the baseline specs look like Mac-wise for getting this model to run? Am I looking at a 96gb? 128? 256?

27-30B in general seems to be the level where you actually start having decent models. I just wish consumer hardware hadn't stagnated so much that we can't easily go higher than that, and that even running those requires a $5k machine now.

I was interested to see that Qwen3.5-122B-A10B narrowly beat Qwen3.6-27B on Donato Capitella's SWEBench-verified-mini run with a similar 128GB UMA architecture.

https://pi-local-coding-bench.dev

I've tested it extensively for actual local development for my job, and hard disagree here. It's a waste of time to use it. Wish it were not true.

> What it does:

>

> --jinja for tool calling support

Pretty sure this flag hasn't done anything for a while. It's enabled by default since ~November of last year

I've come from the future to say Qwen 3.7 27B is just around the corner and slaps!

Something I find really confusing from this post is the MLX versions of the model running much slower. As I understand it, these model versions are meant to take advantage of Apple Silicon and MacOS APIs, and should produce better/faster results. Any insight into what’s happening here?

How does llama.cpp use the GPU efficiently as opposed to MLX?

Is there any way to use MLX and GPU at the same time? Or does memory become a big problem?

TBH, I never understood Apple hyping these neural cores because I didn't think anyone actually uses them except maybe certain photo/video editing software.

If I can generate voice at the same time as video, that would be useful.

And AI companies will continue to buy up all the silicon to make this prohibitively expensive to run at home.

Strix Halo user here. While Qwen 3.6 27B exhibits remarkable intelligence density, I will still take unsloth's dynamic IQ2_XXS of Minimax M2.7 over Q8_0 Qwen 3.6 27B any day of the week, and this isn't just because of generation speed either. I wrote my own custom harness, and I get hallucinated tool call parameters and bizarre invocations with Q3.6 27B even at Q8_0, but no issues with the IQ2_XXS of M2.7.

Local models are great for a lot of things past just software development. We need to move towards solving other real world problems vs just building software. I've been focused on that with TxtAI (https://github.com/neuml/txtai) for 6 years now.

none of these local models are good for development, complete waste of time. nobody has $100k+ hardware sitting around at home to actually run a good model

Gemma4 31B with MTP enabled is faster and I feel a bit stronger at coding. Either one can run in 32GB VRAM or unified RAM with some tuning (3 bit weights, 8 bit kv cache)

Qwen's new AgentWorld model is good too: https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B

I'm running the NVFP4 alongside Gemma4 at the same quant on an OEM Spark

Very capable lora adapters are surfacing but it seems they are very niche.

Spent a week trying to get sensible results out of llama 3.3 At one point it even simulated doing the work, log output and everything and when I challenged it about the missing artefacts it actually started questioning my intelligence. Seems appropriate for a Zuck enterprise.

Qwen on the other hand got straight to work with astonishing competency on the same system.

From what I read llama3 needs beefier compute to reliably invoke tools, which I presume relates to it focussing more on simulating AGI rather than being a useful tool.

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This is kind of like saying grass is green to be honest