> Having a machine that can run some modest local LLMs, like the Gemma 4 12B, is really worth it.

Cloud models are (much) faster, they don't consume so much power/generate heat, they have much bigger (LLM) context, they're much more precise and they have a much wider (engineering) context of the given problem.

Except privacy and use cases that are blocked by cloud models (e.g. reverse engineering), local LLMs are currently an expensive toy.

When I try to program with a local LLM (I'm on a 32/128 GB system), I end up wasting time compared to a cloud LLM.

Exactly. The distinction between the various layers in "AI" systems is pretty vague to the newcomer. What is the "model" vs. the engine "running" it vs. weights?

I don't recall any previous tech stack that was barfed onto the scene with so little background or reference material, going from zero to endless undefined jargon... and no primer in sight.

For people who demand an understanding of their tools, it's a lot of work. I recognize the value of "AI" in performing the tasks I'd have to do manually; for example, keeping the data structures of my front- and back-ends in sync in a project. But do I want to interrupt my development and take weeks off to digest all of these tools?

And if I do, I want to run the show and fully understand it. And like you, I think that's best done locally.

I agree with the learning aspect, but I have another motivation. I suspect that closed models might become too expensive to run for personal hobbyist use. I’ve been planning to buy a 64GB machine just to allow the limited local models this enables.

I'd say give it some time for the dust to settle. This field badly needs standardized benchmarks even before the conversation around model goodness can start.

> Having a machine that can run some modest local LLMs, like the Gemma 4 12B, is really worth it.

Agree having a powerful machine is really worth it in general for professionals, but strong disagree that running local LLMs has anything to do with it. It's hard enough as it is getting a good ROI on your time/money prompting/wrangling with frontier models. IMO leaning on the comparatively limited capabilities of local LLMs is best avoided in favor of keeping your own personal coding skills fresh and continuing to learn new ones.

> I totally struggled to find the right frame of mind to explore any of this stuff without feeling defeated and bamboozled.

I found LM studio to be a nice starting point. Frindlier and more featureful than Ollama and not as intimidating as llama.cpp (though you will want to use that eventually)

Honestly your best bet is to buy a $20 Claude subscription, ask Claude to set it all up with Pi and llama.cpp and come back in 20 minutes after a cup of coffee. This is also a good idea because it will help set expectations of what a local model can do vs. a frontier model.

I've setup to local paradigms for local coding:

- opencode with it's webui

- deer-flow with it's research/powered front end

They both run websites so you don't have to baby sit them (eg, keep your mac open). I've build a pdf compressor over a few days by first having deer flow try and research the frameworks and pipeline. It stalls out because its not really a fluid programmer. Once it stalls out, I transferred it (manually for now) to opencode and it's refactoring it because it's just a collective bundle of sticks and it needs a lot of testing to tweak out the limited scop context. LLMs can't really hold large scopes (locally anyway, from what I've read from HN, it's possible with longer context).

It'll complete in a few days with maybe 3-4 hours of full attention interaction, but it's running 3x that without my attention. Obviously, if I paid more attention it'd run quicker, but since it's local, it's not pumping out large volumes of code, it's mostly looping over tests and capabilities as observed.

It's running Qwen3.6 35B MoE on a AMD 128GB strix halo. If I switched to the dense models, perhaps it'd be smarter, but the trade off seems to be much slower gen.

> I totally struggled to find the right frame of mind to explore any of this stuff without feeling defeated and bamboozled. Because it's just huge, exhausting, jargon-drenched, unknowable, and I am over the hill at fifty-plus.

Hello, my brother, just know that you have a fellow passenger in life at the same age who thinks the same thing. I agree that the local stuff is helping my understanding a LOT.

However, my gut feel as someone who got to experience the TeleBomb after the DotBomb is that the obfuscation is INTENTIONAL--it's neither you nor your age. I remember asking people to explain to me what the OC-768 startup endgame was when roughly 10 OC-768 links could carry the world's traffic at the time--and everybody giving me blank looks. The AI Bubble has the EXACT same feel as the Telecom Bubble--just bigger.

What I really wish is that I could find a VPS-type provider where I could toss things into their NVIDIA/AMD machines for an hour or two. Alas, all of the providers seem to want massive paperwork and huge minimum purchases.

I can't wait for the bubble to pop so that we mere mortals can finally build with this stuff.

I just got Claude to download and install all the models and servers and agents and prepare all the launch scripts for me... no need to learn, just ask it to do it for you

> 48GB of VRAM with, say, two 3090s

So like... $2000+ just for the used GPUs? Plus I assume it's considerably more effort to get it working.

The tweet you link shows "Qwen 3.6 35b NVFP4 - 256k ctx, 110 tok/s", but I'm getting only half that, around 50 tok/sec, on a DGX Spark with Qwen3.6-35B-A3B-NVFP4 (via vLLM) plus speculative decode w/EAGLE3. I'd be ecstatic to see 110 tok/sec and I wish they had some more sourcing for the exact config, because it's double what I'm getting.

edit - after actually reading the tweets (had to use xcancel) and visiting the source git repo, switching to MTP for speculative decode makes things a hell of a lot faster, and the abliterated model plus dflash makes it even faster! I'm now seeing 70-90 tok/sec for most stuff. I like!

For a MBP I have 48 GB of RAM M5 Pro. It runs at about 12-14 t/s at Q4, you could probably optimize it further. RAM is not a limitation but overall memory bandwidth. Q8 is slower. 35B A3B Qwen is quite speedy, but a little less accurate. With Qwen 3.6 27B dense I can squeeze a 9B parameter model and use that for fast analysis or code scanning while 27B is churning on a task in the background. It is tight, but totally reasonable.

The real sweet spot for Qwen 27B is getting it on something like a Dual 3090 system or some other config where it can blaze at 50-80 t/s and that costs well under 6K currently. It is a surprisingly capable model. Using something like GLM for orchestration, specs, task farming and then letting Qwen churn is relatively inexpensive.

Overall I recommend people try models of this class out using OpenCode and some for pay service to experiment with them and understand how they work. I find they are very useful.

Long term, I am convinced enough that if I wanted to use local models for any number of reasons I would be okay investing in a dual GPU box. The Mac is not fast enough for me and M5 Max is just too expensive relative to GPU linux box. Still, it is nice to have the models local ON the laptop and it is useful for what I care about locally.

At 24GB, Gemma 4 31B QAT will be better and give more concise answers. This post is mostly about unquantized results, so it's less relevant and I can't say much about as I haven't tested Qwen or Gemma via cloud API or unquantized locally. All I can say is locally, quantized in a 24GB scenario, Gemma 4 31B is better in my tests which are mostly reasoning or C programming related.

Gemma 4 is the only model series at this parameter scale I've seen correctly answer some of these. One of the answers even made me re-evaluate what I thought the correct answer was, which I did not expect.

When I look at the Artificial Analysis numbers, I can see that some things about Qwen 3.6 look inflated as a result of either metrics that weren't measured yet for Gemma 4 31B, or for metrics that just aren't going to be relevant in a lot of the essential tasks. In a lot of the relevant metrics, Gemma 4 is either better or on par.

Then once it's all quantized all those benchmark results will be hurt, and Gemma 4 QAT has better quantized performance. I think it's more competitive unquantized than people give it credit for and way better quantized than people give it credit for.

Qwen 3.6 clearly isn't legitimately bad and maybe it's quite nice at fp16, but it was a disaster quantized in a 24GB scenario by comparison.

I'd go for at least 32GB+. It'll fit in 24GB but leaves you little to no room for context, and that's at 4-bit quantization.

If you want to run unquantized, you definitely need 128GB.

And if you go for actual GPUs it'll run much faster, I'd say 24gb may be pushing it for context, but my 5090 with 32GB VRAM is usually somewhere between 60 to 100 tok/s with mtp and 2-3k tok/s for prompt processing. I'm not sure what they cost now but it's definitely still quite far from the macbook, and there's also some other 32GB GPUs that are considerably more affordable

a computer with 24 GB VRAM is at least $3000

Companies are already shipping everything to Microsoft or Google and 17 other companies, just the cost of doing business.

It's much slower, and often quantized

I think the argument isn't that local is cheaper - it's that local is doable and delivers unparalleled privacy.

am i right in assuming your code is closed-source?

i'd expect anything on github for example to be already in their training set or is training on actual usage more useful to them?

That 3090 is going to burn 750W and it will still cap you at a 4 bit quant and ~48K context. Here's someone who worked through it:

https://github.com/noonghunna/qwen36-27b-single-3090

Flies though (50-70tps is impressive for a model this smart)

I went through roughly the same process to get it working on my M2 Macbook Pro... at awful speeds of course, since models like this one are mostly bound by memory bandwidth.

I've been running it on my 48GB MBP too and it's not particularly great. Super slow and not near enough to the quality provided by even Claude Sonnet.

How much does one of those cost in the US? Here in Brazil, your notebook is worth as much as a used Honda Fit, which seems absolutely insane. For comparison, the ThinkPad I'm currently running cost me 1/20 of how much this MBP costs here, leaving me with over $8.000 to spend with LLM inference (if I actually spent money with that).

I get ~55 Tok/s on my framework desktop with the 35B A3B q8 model, and so far am also very happy with the coding performance.

There are several variants of Qwen 3.6, the MoE models are performant on Strix Halo, but the 27B dense model (the one spoken about in TFA, and generally regarded as the best of the group in terms of quality) is not so performant: https://kyuz0.github.io/amd-strix-halo-toolboxes/

How did you buy 3 V100's for $1500??

I got mine at the same price point, and I've been pretty pleased with it. Tailscale lets me use it from my ultrabook / lightweight laptop, no burning lap or crazy fan noises. Desktops with the amd ai+ 395 are still fairly affordable for what they can do.

I haven't tried it with https://lemonade-server.ai/ yet but I just might give it a shot.

Memory requirements of newer models will increase, so while the hardware may last 10 years it won't be able to run the latest models for 10 years.

In 5-10 years, incremental cloud tokens will be far cheaper (likely but not guaranteed).

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if you need to ensure that, then just back up the model and buy hardware if the need arises

Using some rough napkin (well, spreadsheet) math, if you ran Qwen 27B for every minute every day at the current price of $0.195/$1.56 with a 2:1 input to output ratio (eg. agentic coding) at the advertised 22 tps it would take you just about 11 years to get to ~$5000 spent.

Disclaimer: There's a 35% sale from Alibaba right now. And I'm not accounting for input tokens going faster than output tokens.

Are you comparing the cost of hosted Opus to running Qwen 3.6 locally? That doesn't really seem fair.

Yikes, you broke the site guidelines badly with this post. Could you please review https://news.ycombinator.com/newsguidelines.html and stick to them?

You're welcome to make your substantive points thoughtfully, just not aggressively.

> maybe tell us how much a non-Apple system that you can run that (probably similarly or faster) would cost?

Ryzen AI Max 395+ with 128GB of unified memory can be found around $3-4k.

But 27B isn't that large, either, especially if you are ok with the quantized models. So this laptop choice seems to more be a "because they had it" rather than "this is what's necessary for this particular workflow"