- 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)
I'd imagine you'd have a bunch of cheap ones in the house that are all WiFi + Mic + Speakers, streaming back to your actual voice processing box (which would cost a wee bit more, but also have local access to all the data it needs).
You can see quite quickly that this becomes just another program running on a host, so if you use a slightly beefier machine and chuck a WiFi card in as well you've got your WiFi extenders.
As compared to Alexa? I bought their preview hardware (and had a home-rolled ESP32 version before that even) and things are getting closer, I can see the future where this works but we aren't there today IMHO. HA Voice (the current hardware) does not do well enough in the mic or speaker [0] department when compared to the Echos. My Echo can hear me over just about anything and I can hear it back, the HA Voice hardware is too quiet and the mic does not pick my up from the same distances or noise pollution levels as the Echo.
I _love_ my HA setup and run everything through it. I'd like nothing more than to trash all my Echos, I cam close to ordering multiple of the preview devices but convinced myself to get just 1 to test (glad I did).
Bottom line: I think HA Voice is the future (for me) but it's not ready yet, it doesn't compare to the Echos. I wish so much that my Sonos speakers could integrate with HA Voice since I already have those everywhere and I know they sound good.
[0] I use Sonos for all my music/audio listening in my house so I only care about the speaker for hearing it talk back to me, I don't need high-end audiophile speakers.
But really my use case is as simple as
1. Wake word, what time is it in ____
2. Wake word, how is the weather in ____
3. Wake word, will it rain/snow/?? in _____ today / tomorrow / ??
4. Wake word, what is ______
5. Wake word, when is the next new moon / full moon?
6. Wake word, when is sunrise / sunset?
And something similar like that
Even gave it a custom wake word, she's Janet now.
HA is pretty clunky and there's a lot of manual setup. But I have a voice assistant contained entirely within my local infrastructure. I'm even planning to wire it up to my local ollama server for actual AI inference behind it.
So far it's exactly as crappy as Alexa, but only because I haven't waded deep enough into configuration. I'm okay with tools being crap when it's my fault instead of the tool being crap because it doesn't make Amazon enough money.
Wowsers I did not know this was a thing; TIL, thanks!
You have all of the different components:
* you can use a number of things for the interactive devices (any touchscreen device, buttons, voice, etc)
* have it HA do the basic parsing (word for word matching), with optionally plugging into something more complex (cloud service like ChatGPT, or self-hosted Ollama or whatever) for more advanced parsing (logical parsing)
Every part of the ecosystem is interchangeable and very open. You can use a bunch of different devices, a bunch of different LLMs to do the advanced parsing if you want it. HA can control pretty much everything with an API, and can itself be controlled by pretty much anything that can talk an API.
Great timing as I was looking into it yesterday as was thinking about writing my own set of agents to run house stuff. I don't want to spent loads of time on voice interaction so HA wakeword stuff would've been useful. If not I'll bypass HA for voice and really only use HA via mcp.
I can do fw dev for micros...but omg do I not want to spend the time looking thru a datasheet and getting something to run efficiently myself these days.
The market is not ready for building this due to costs etc. not because the big companies block them or anything. And nvidia is not selling subscriptions at all.
Yeah because dynamic digital price signs in shops based on what data vendors have about you and AI can extract from it are such fun! Total surveillance. More than what's already happening. Such fun!
> 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.
Their output is not great so they get downvoted and spotted quickly.
You can paste any article and chatgpt (took the most laymen AI thing) and just writing summarize this article https://byteshape.com/blogs/Qwen3-30B-A3B-Instruct-2507/
can give you insights about it.
Although I am all for freedom, one forgets that this is one of the few places left on internet where discussions feel meaningful and I am not judging you if you want AI but do it at your own discretion using chatbots.
If you want, you can even hack around a simple extension (tampermonkey etc.) where you can have a button which can do this for you if you really so desire.
Ended up being bored and asked chatgpt to do this but chatgpt is having something wrong, it got just blinking mode so I asked claude web (4.5 sonnet) to do it and I ended up building it with tampermonkey script.
Created the code. https://github.com/SerJaimeLannister/tampermonkey-hn-summari...
I was just writing this comment and I just got curious I guess so in the end ended up building it.
Although Edit: Thinking about it, I felt that we should read other people's articles as well. I just created this tool not out of endorsement of idea or anything but just curiosity or boredom but I think that we should probably read the articles themselves instead of asking chatgpt or LLM's about it.
There is this quote which I remembered right now
If something is worth talking/discussing about, its worth writing
If something is worth writing, then its worth reading.
Information that we write is fundamentally subjective (our writing style etc with our biases etc.), passing it through a black box which will try to homogenify all of it just feels like it misses the point.
I tried the q4 quantization when it came out and didn't find it to be great for my coding use case.
Realistically, the biggest models you can run at a reasonable price right now are quantized versions of things like the Qwen3 30B A3B family. A 4-bit quantized version fits in roughly 15GB of RAM. This will run very nicely on something like an Nvidia 3090. But you can also use your regular RAM (though it will be slower).
These models aren't competitive with GPT 5 or Opus 4.5! But they're mostly all noticeably better than GPT-4o, some by quite a bit. Some of the 30B models will run as basic agentic coders.
There are also some great 4B to 8B models from various organizations that will fit on smaller systems. A 8B model, for example, can be a great translator.
(If you have a bunch of money and patience, you can also run something like GPT OSS 120B or GLM 4.5 Air locally.)
OpenRouter gives you $10 credit when you sign up - stick your API key in and compare as many models as you want. It's all browser local storage.
Don't need patience for these, just money. A single RTX 6000 Pro runs those great and super fast.
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...
They still aren't useful like large LLMs, but for things like summarization, and other tasks where you can give them structure but want the sheen of natural language they are much better than things like the Phi series were.
./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]
llama-server -m /Qwen3-30B-A3B-Instruct-2507-GGUF:IQ3_S --jinja -c 4096 --host 0.0.0.0 --port 8033 Got <= 10 t/s Which I think is not so bad!
On AMD Ryzen 5 5500U with Radeon Graphics and Compiled for Vulkan Got 15 t/s - could swear this morning was <= 20 t/s
On AMD Ryzen 7 H 255 w/ Radeon 780M Graphics and Compiled for Vulkan Got 40 t/s On the last I did a quick comparison with unsloth version unsloth/Qwen3-30B-A3B-GGUF:Q4_K_M and got 25 t/s Can't really comment on quality of output - seems similar
https://github.com/ikawrakow/ik_llama.cpp and their 4Bit-quants?
Or maybe even Microsofts Bitnet? https://github.com/microsoft/BitNet
https://github.com/ikawrakow/ik_llama.cpp/pull/337
https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf ?
That would be an interesting comparison for running local LLMs on such low-end/edge-devices. Or common office machines with only iGPU.
I have not figured out what models that fit in the available memory (say 16Gb) that would be best for doing this. A CPU model I can run on a laptop would be nice. The models I have tried are much smaller than 30B.
I'm able to get 6-7 tokens/sec generation with 10-11 tokens/sec prompt processing with their model. Seems quite good, actually—much more useful than llama 3.2:3b, which has comparable performance on this Pi.
There have been a lot of boards and chips for years with dedicated compute hardware, but they’re only so useful for these LLM models that require huge memory bandwidth.
It's just that practically nothing uses those NPUs.
The industry has to copy CUDA, or give up and focus on raster. ASIC solutions are a snipe chase, not to mention small and slow.
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...
It's accuracy across GSM8K, MMLU, IFEVAL and LiveCodeBench.
They detail their methodology here: https://byteshape.com/blogs/Qwen3-4B-I-2507/
Original: 11tok/s Byteshape: 16tok/s
Quite a nice improvement!
Going from BF16 to 2.8 and losing only ~5% sounds odd to me.
They detail their methodology here: https://byteshape.com/blogs/Qwen3-4B-I-2507/
It punches well above the weight class expected from 3B active parameters. You could build the bear in Spielberg's "AI" with this thing, if not the kid.
In a nutshell: LLMs generate tokens one at a time. "only 3B parameters active a a time" means that for each of those tokens only 3B parameters need to be fetched from memory, instead of all of them (30B).
MoE models still operate on token-by-token basis, i.e. "pot/at/o" -> "12345/7654/8472". "Experts" are selected on per-token basis, not per-interation, so "expert" naming might be a bit of a misnomer, or marketing.
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.