A 30B Qwen model walks into a Raspberry Pi and runs in real time

There is a huge market segment waiting here. At least I think there is. Well, at least people like me want this. Ok, tens of dollars can be made at least. It is just missing a critical tipping point. Basically, I want an alexa like device for the home backed by local inference and storage with some standardized components identified:

- the interactive devices - all the alexa/google/apple devices out there are this interface, also, probably some TV input that stays local and I can voice control. That kind of thing. It should have a good speaker and voice control. It probably should also do other things like act as a wifi range extender or be the router. That would actually be good. I would buy one for each room so no need for crazy antennas if they are close and can create true mesh network for me. But I digress.

- the home 'cloud' server that is storage and control. This is a cheap CPU, a little ram and potentially a lot of storage. It should hold the 'apps' for my home and be the one place I can back-up everything about my network (including the network config!)

- the inference engines. That is where this kind of repo/device combo comes in. I buy it and it knows how to advertise in a standard way its services and the controlling node connects it to the home devices. It would be great to just plug it in and go.

Of course all of these could be combined but conceptually I want to be able to swap and mix and match at these levels so options here and interoperability is what really matters.

I know a lot of (all of) these pieces exist, but they don't work well together. There isn't a simple standard 'buy this turn it on and pair with your local network' kind of plug and play environment.

My core requirements are really privacy and that it starts taking over the unitaskers/plays well together with other things. There is a reason I am buying all this local stuff. If you phone home/require me to set up an account with you I probably don't want to buy your product. I want to be able to say 'Freddy, set timer for 10 mins' or 'Freddy, what is the number one tourist attraction in South Dakota' (wall drugs if you were wondering)

In case anyone else clicked in wondering what counts as "real time" for this:

> On a Pi 5 (16GB), Q3_K_S-2.70bpw [KQ-2] hits 8.03 TPS at 2.70 BPW and maintains 94.18% of BF16 quality.

And they talk about other hardware and details. But that's the expanded version of the headline claim.

Is there a good place for easy comparisons of different models? I know gpt-oss-20b and gpt-oss-120b have different numbers of parameters, but don't know what this means in practice. All my experience with AI has been with larger models like Gemini and GPT. I'm interested in running models on my own hardware but don't know how small I can go and still get useful output both for simple things like fixing spelling and grammar, as well as complex things like programming.

I've been super impressed by qwen3:0.6b (yes, 0.6B) running in Ollama.

If you have very specific, constrained tasks it can do quite a lot. It's not perfect though.

https://tools.nicklothian.com/llm_comparator.html?gist=fcae9... is an example conversation where I took OpenAI's "Natural language to SQL" prompt[1], send it to Ollama:qwen3:0.6b and the asked Gemini Flash 3 to compare what qwen3:0.6b did vs what Flash did.

Flash was clearly correct, but the qwen3:0.6b errors are interesting in themselves.

[1] https://platform.openai.com/docs/examples/default-sql-transl...

I have been seeing these stories about bigger AI models that somehow work on small devices like a Raspberry Pi. One example really caught my eye lately. It was not just some quick show off thing. The model could interact and respond right away in real time. ~

That got me thinking again about what practical even means when it comes to AI running on the edge. Like, away from big servers.

I came up with a basic way to look at it. First, capability. That is, what kinds of tasks can it handle decently. Then latency. Does it respond quick enough so it does not feel laggy. There are also constraints to consider. Things like power use, how much memory it needs, and heat buildup.

The use case part seems key too. What happens if you try to take it off the cloud and run it locally. In my experience, a lot of these edge AI demos fall short there. The tech looks impressive, but it is hard to see why you really need it that way.

It seems like most people overlook that unclear need. I am curious about how others see it. Local inference probably beats cloud in spots where you cannot rely on internet, or maybe for privacy reasons. Or when data stays on device for security.

Some workloads feel close right now. They might shift soon as hardware gets better. I think stuff like voice assistants or simple image recognition could tip over.

If someone has actually put a model on limited hardware, like in a product, what stood out as a surprise. The thermals maybe, or unexpected power drains. It feels like that part gets messy in practice. I might be oversimplifying how tricky it all is.

Had to try this on my laptop (vs my non-byteshape qwen3-30-a3b)

Original: 11tok/s Byteshape: 16tok/s

Quite a nice improvement!

We need custom inference chips at scale for this imho. Every computer (whatever formfactor/board) should have an inference unit on it so at least inference is efficient and fast and can be offloaded while the cpu is doing something else.

I've just tried replicating this on my Pi 5 16GB, running the latest llama.cpp... and it segfaults:

    ./build/bin/llama-cli -m "models/Qwen3-30B-A3B-Instruct-2507-Q3_K_S-2.70bpw.gguf" -e --no-mmap -t 4
    ...
    Loading model... -ggml_aligned_malloc: insufficient memory (attempted to allocate 24576.00 MB)
    ggml_backend_cpu_buffer_type_alloc_buffer: failed to allocate buffer of size 25769803776
    alloc_tensor_range: failed to allocate CPU buffer of size 25769803776
    llama_init_from_model: failed to initialize the context: failed to allocate buffer for kv cache
    Segmentation fault

[Edit: had to add -c 4096 to cut down the context size, now it loads]

How can I use ByteShape to run LLMs faster on my 32GB MacBook M1 Max? Or has Ollama already optimized that?

I can’t wait to get home and try this on my Pi. Past few months, I’ve been building a fully local agent [0] that runs inference entirely on a Raspberry Pi, and I’ve been extensively testing a plethora of small, open models as part of my studies. This is an incredibly exciting field, and I hope it gains more attention as we shift away from massive, centralized AI platforms and toward improving the performance of local models.

For anyone interested in a comparative review of different models that can run on a Pi, here’s a great article [1] I came across while working on my project.

[0] https://github.com/syxanash/maxheadbox

[1] https://www.stratosphereips.org/blog/2025/6/5/how-well-do-ll...

Does anyone have any use-cases for long-running, interesting tasks that do not require perfect accuracy? Seems like this would be the sweet spot for running local models on low-powered hardware.

So basically the quantization in a byteshape model is per-tensor and can be variable and is an "average" in the final result? The results look good - curious why this isn't more prevalent! Would also love to better understand what factors into "accuracy" since there might be some nuance there depending on the measure.

Is there something different about that accuracy measure? i.e. relative to perplexity term usually used

Going from BF16 to 2.8 and losing only ~5% sounds odd to me.

This is impressive work on quantization. It really validates the hypothesis that software optimization (and proper memory management) matters more than just raw FLOPs.

How would this run on a Jetson Orin Nano dev kit?

I feel like calling it a “30B” model is slightly disingenuous. It’s a 30B-A3B. So only 3B parameters is active at a given time. While still impressive nevertheless, being able to get 8T/s for a “A3B” compared to a dense 30B is very different.

GPT-OSS-20B is only 11.2GB. Should fit in any 16GB machine with descent context without any quality degradation.

We're approaching the point where we could have sophisticated sound controlled sex toys.

LLMs are, by definition, real time at any speed. 50,000 tokens per second? Real time. Only 0.0002 tokens per minute? Still real time.

Eight tokens per second is "real time" in that sense, but that's also the kind of speeds that we used to mock old video games for, when they would show "computers" but the text would slowly get printed to a screen letter for letter or word for word.

I'm just pulling stuff out of my butt here but is this an area where an fpga might be worth it for a particular type of model?