Building a Fully Offline AI Stack: A Proposal
Hey folks, this one's going to be short and simple. No fancy formatting or morbid humor th...
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This is exactly my kind of jam—especially as someone who values autonomy and privacy in AI tooling. Your plan for a fully offline, self-contained AI stack is not only ambitious but deeply refreshing. The nod to GPU and storage considerations highlights your practical, real-world awareness—solid groundwork for anyone planning to replicate or adapt your setup.
I’d absolutely love a detailed breakdown of the tools and specs, especially your handling of resource constraints and fallback setups. And if you're open to expanding into agent-based architectures, your post could become a gateway for building an AI agent that operates fully offline and customizable to each user's hardware.
Thanks for doing this—for real, it's the authentic, no-vendor-cloud vibe that sets work like yours apart. Count me in as a keen reader and eager tester!
Haha, I can tell your reply’s got a bit of AI polish—it’s smooth enough it could’ve been drafted by a model. No shade though, I actually filtered this one through AI myself just to keep the tone right. I’m tuned into that wavelength and can usually spot it a mile away. 😉
Really appreciate the support—it means a lot. Quick question I throw to everyone: what’s your setup like in terms of OS and GPU VRAM? Most folks chiming in so far are Windows-based. I’ll be building out support for Windows too, but I may need to spin up a Discord just for that crowd since I’ll have to rebuild/test on that side to offer proper troubleshooting.
On the agentic workflows—if you mean full corporate-style automation, that’s not really my lane. There are lighter-weight, purpose-built options for that. But if you mean letting the AI coordinate across offline tools, that’s actually part of the tool-set I provide. The stack already supports multi-model input and can hook into local utilities, so you could absolutely build an agent layer on top if you wanted. My focus is more on creativity and play than business pipelines—but the architecture leaves the door open for people to extend it however they like.
Either way—authentic or AI-filtered—you’re in the right place. Chaos is my element. If you’re ever serious about building a fully agentic stack though, that drifts into my freelance lane. Not something I’d spin up in a casual dev.to thread, but I’m always open to being contacted on Discord if someone wants to go pro with it.
check out WhisperX, it has TTS and can do word and letter-level timestamps as well as speaker diarization. There are different model sizes you can use. I wrote a little server that can processes a wav files and return JSON with word timestamps and speaker diarization. It is handy for adding animated subtitles (TikTok style). Another option is to use forced alignment (like NeMo Forced Alignment, or NFA). This can be more accurate because you give it the audio and the words that were spoken, and then it gives you timestamps and makes sure that the words are accurate and based on your input text.
That sounds almost perfect. Is it quick and accurate for me to just speak naturally to my AI? that's kind of the big thing I need personally.
It doesn't take that long to process your voice. It does depend on how long your utterance is, the size of the model you are using, your hardware, your pronunciation/enunciation.. you should probably just test it out and build a benchmark. I typically just feed TTS back into STT, but I'll probably be trying this on my own voice, too. You can also put the output text through an LLM doing STT to attempt corrections for words and phrases that the STT got wrong.
Major props for tackling a full-featured, offline stack. The unified front-end is the holy grail for these kinds of projects.
Please count this as a strong vote of interest. I'd love to see the component list!
I’ll happily count your vote as a strong one! My main question: which OS are you running? As far as I know, everything works on Windows, but beyond the LLM and Stable Diffusion, I haven’t fully tested TTS or domain integration there. I have a dual boot setup and my long term goal is accessibility regardless of platform.
I'm mainly on Linux (Ubuntu/Fedora), I think I could try to patch your solution to Linux, hopefully it will just work.
I run my own on Linux I'm just looking to support both OS in that regard, but hey once I have it out there, if you have any suggestions for tweaks or anything I'd love to hear them! I'm pretty new to Linux (I use MInt).
I'm planning on setting up a Discord for anyone who needs help along the way and I'd love to have someone like yourself in there to help. I built it out over months and didn't document it so it's hard for me to do a proper full tutorial without a clean environment to rebuild it.
This sounds really interesting. An offline, all-in-one AI stack would solve a lot of privacy and reliability concerns. Curious to see how you balance GPU demand with usability—definitely worth a deeper write-up.
That will come down to personal preference a bit and running models that are a good match for your available resources. I'm hoping to drop the run down later today!
I think that setup is a very impressive effort to develop a different perspective to use the IA capabilities, your article gives me a good view to understand the open-source IA advantages thank you again for your perspective.
Love the energy here. A few thoughts from running this kind of thing in anger:
The GPU/CPU tax is real. The “Franken-stack” works great until you try to run TTS + vision + LLM inference all at once and your fans spin like a jet engine. Orchestration matters more than raw horsepower, deciding when to call which model saves you far more than shaving another 5% off GPU load.
Docker pain on Linux tracks with my experience. I usually end up with a hybrid: local bare-metal for hot paths, containers for the ancillary stuff. Keeps API domain weirdness isolated.
Cross-platform is always a tarpit. My rule: pick the platform you’d actually deploy on in prod and stabilise there first. Windows being “easier” only lasts until you try scaling.
STT: Vosk or Whisper small models will get you 80% of the way. They’re not perfect, but fine for orchestration loops.
Biggest takeaway: don’t just think in terms of “feature parity with ChatGPT.” Think in terms of consistency, safety, and orchestration, otherwise you’ll end up with a toddler with a super brain opening every toy box at once.
Would read a full write-up, especially if you document not just what works, but what breaks. That’s where the real value is.
Your absolutely right on all accounts. I can feel your pain. I'm not sure if you read my other article about it but, well I basically wrote an entire article about the pain lol. Mine doesn't use docker at all. I kind of just see it as an added dependency. I'm running 2 GPUS, an onboard one in my gaming laptop and an external through the Alienware graphics amplifier. My Linux stack runs great which is why I offered to layout the tools and models im using. Feature oarity with ChatGPT was the original motivator but not the end goal once I got going. If you haven't tried the GPT-OSS 20B model yet, give that one a go. It's set up in a way that doesn't hog the GPU like a normal 20B model would. It's even lighter then a 13B model. its one of the smartest I have used but I keep the reasoning to low or medium for speed because it is slower then my 13B models, but you can always dial it up instead of down if you have the resources to spare.
Unfortunately I built it out over the span of a few months and didn't document anything so initially I would just be detailing tools I know that play nice together, things to consider when setting them up (such as GPU tax and any other caveats) but it wouldn't be a full on tutorial because that would require me rebuilding the entire thing and I'm just not into that at this time.
regarding cross platform thats a long term goal but for now since im running dual boot I would be building out my Linux stack again on Windows and that's mostly because its a clean environment over there so I can document it and make a tutorial easier since I know I'm intending to make one going in as opposed to 3-4 months after I had most of it done.
Out of Whisper or Vosk which do you recommend? I have about 4gb left of vram on the 2nd GPU lol.
Down the road I'll be building out a server with 2 proper GPUS to run it all as opposed to a laptop from 6 years ago with an EGPU carrying the weight.
Ha, yep, sounds familiar.
I totally get not wanting to rebuild just to document. My trick: don’t rebuild, instrument. Next time you spin a component, log every install step, patch, and config flag to a scratchpad. It’s “living docs” without the overhead. You’ll thank yourself six months later.
On GPUs; Running dual cards with an external amp is… heroic. Respect. But yeah, orchestration beats raw metal here too. Partition tasks smartly: route cheap inference (summaries, metadata) to the weaker GPU; push the heavy reasoning or vision to the stronger one. Keeps both busy without bottlenecking.
I haven’t run GPT-OSS 20B yet but agree, size isn’t everything. Orchestration and reasoning depth matters. Dial reasoning like a throttle: low for utility, high only when you need correctness. Think scalpel, not hammer.
On the STT side, if you’ve got 4GB spare VRAM, I’d lean Whisper tiny or small. It’ll still fit, runs smooth, and the accuracy is noticeably better than Vosk. Vosk only really shines when you’re stuck CPU-only, but since you’ve already got GPU cycles to play with, Whisper’s the safer long-term bet. Think of it as spending cycles on consistency now rather than patching accuracy issues later.
Down the road, when you’ve got that dual-GPU server, the biggest win isn’t raw speed, it’s headroom to orchestrate. Consistency loops, retries, validation pipelines, all the stuff that makes the system trustworthy. That’s when this “pain log” turns into a proper stack.
Typically I document things in whatever random text editor I have and I regret not doing that cause its going to hurt a little when I need to set it all up again on a new pc lol. I was going to make a full on build script to automate the entire process but theres no way I could do that without just rebuilding the stack and documenting as I go.
Reasonig wise for GPT-OSS it's nice to have but the drawback with my setup is that in order to change it between low medium or high, I have to go into LM Studio and eject the model and load it back in for the change to take effect. I'm debating trying to find a dynamic solution but realistically if I'm willing to wait 2 minutes for it to reason, I'm probably doing some heavy coding and I'd be better off just running deepseek-coder. The only real snag I run into with GPT-OSS is that even with rolling window or truncate middle enabled, it will eventually max out the context length and I have to go start a new chat. Thats more of an LM Studio thing, im sure as they update now that it is out they will account for that.
I like the idea of STT and another commenter suggested I try WhisperX so Whisper in whatever form seems to be the choice here, im just worried about processing time. I'm sure I can do some streeaming optimization or settings changes to get Chatterbox TTS to start responding with voice once the LLM has generated say a paragraph or so but as it stands I have to wait for the full LLM response to generate before TTS processing begins. Chatterbox does stream it back in chunks so once it starts it just keeps going with only a small pause at the start but I have a bit of work to do to make it feel more fluid. It's a nice to have though and I enjoy being able to just ask a question, switch tabs and in under a minute its generally going on a lengthy monologue for me lol.
Fortunately running what I have now I have already learned the importance of proper orchestration. There is no way I would be able to run an average 13B model, SD, and TTS at the same time. When I tried it maxed out the 3060 and would dump the LLM out of LM Studio causing me to have to reload the model, which by then my GPU is occupied so I had to run a smaller one to continue which is why the TTS runs on the 1070 instead. I'm iffy on using up that last 4GB cause I am usually still watching youtube and it still has to handle my GUI and such on my laptop for now so it gives me a bit of room to continue normal operation even with the full stack running :). If the STT is only around 1.5GB or so, give or take, I think that would be fine. I'll just have to give it a try and see.
Id be happy to hear more about it, I have a window gaming rig with a 3090 sitting around just waiting for a workload!
I'd be lying if I said I wasn't a little envious lol. My stack uses a 12GB 3060 as the main GPU. The main limitation would be VRAM. If you don't run the TTS and Image gen at the same time though you are probably in the clear. Ill provide some LLM variants that might still allow all of it to run at once.
Thank you for the input!
A lot of devs, startups, and privacy-focused users are interested — especially those who want control over data, avoid cloud costs, and experiment with custom AI locally
The privacy aspect is a huge bonus of this set up. And you can still use it online through a website while still having all your data stored locally.
Update: I intend to have this out within about 1 weeks time. I think most people are on windows so I'm going to make sure all of the components are available on that OS aswell. I'm pretty busy the next few days but I' hoping to get started on this as soon as possible.
Even just an outline of your choices would be a nice release. I know there are a ton of open source chatgpt clones, but I'd love to hear what worked best for you
Hey i would love to set this up locally , looking forward to hear from you
It should be up in the next few hours. Just keep in mind it's not a fully detailed tutorial or how-to guide. More like me handing you a set of Lego bricks but you have to put them together yourself :p
Yeap got it
A fully offline Franken stack sounds epic..
Also interested, indeed very cool option but ram and cpu eater indeed
How about your system's speed?
spec wise Im running:
1070 laptop GPU (8GB) (Alienware R4 from 2018)
3060 (12GB) through the AGA (external alienware dock)
i7 CPU (I'm unsure which exact model but it's the one that came in the alienware R4)
32GB of ram
The 3060 and i7 do most of the work and right now the 1070 mostly just handles the TTS.
I mean performance. The ChatGPT can reach 100 token/s.
That's very model dependant. Even comparing 2 different 13B paramter models like Deepseek and Mythalion, Mythalion is super fast and Deepseek is the slowest model I have. GPT-OSS lands somewhere in the middle, partially due to reasoning and the time it takes the model to process the prompt. You can fine tune a lot of settings to adjust it but faster often means less accurate.
Local AI setups are finally starting to feel usable without needing a research lab in your garage. Love the DIY spirit here. It feels like the early Linux days, but with LLMs.
I'd be very interested to see how this could all blend together. Been looking around to run things locally, so having a guide would really help!
Still need to invest in hardware, so any recommendations there given the different sizes in amount of parameters and response times would really help :)
I'll be on Linux, but also be tunneling into my local network, so personally opening it up to "the outside" will be a chapter I'll be skipping. I would be interested if there would be a noticeable difference between running it all natively, or through Docker. Maybe something to consider :)
GPU wise in total I have 2, An 8GB 1070 (built into a gaming laptop) and a 12GB 3060 so a total of 20GB of Vram and the full stack requires both GPUs and doesn't leave a lot left.
Thats running 13B param models for the LLM side. If I don't run the TTS, I don't need the 1070. The 12GB 3060 is just enough to run a 13B model and Stable diffusion. any other components are negligible for GPU.
I have an older i7 CPU (circa 2018) so any decent modern CPU is fine.
I'm unsure in regards to docker. I think it would take more disk space as there's a good chance it would be running 3 or more virtual environments at about 5-8.5 gigs each. Disk space is also dependent on what models as you have as they tend to be pretty large. My full stack setup with 5 or 6 LLM models and 2 SD models is about 120GB when all is said in done but I set it up to minimize the number of venvs and so far I still have 2 of them.
The problem I ran into with docker on linux was that when hosting a local server it would append docker to the endpoint address which caused issues for me. I don't use docker much and just wasn't willing to fight with it to find a work around lol.
the newly release GPT-OSS 20B LLM model actually runs a lot less resource heavy then a 13B once it's loaded (it only stores about 3B in memory at any given time) but on my hardware due to it's reasoning capabilities it tends to be a bit slow as where a typical non reasoning 13B model tends to run at a speed on par with ChatGPT 4 on regular internet. (I haven't used GPT5 much).
If you could get a 24GB card, you're laughing but they are pricey to put it mildly :p. I recommend 12+ gb of vram. The more vram, the smarter the model you can run. Deepseek coder is pretty slow on my setup despite it being 13B. Some of that is optimization too. There's plenty of knobs and dials turn that increase or decrease performance depending on what your machine can handle.
I will be honest, I don't get the point...all of the frameworks support Ollama , which makes them offline stacks.
Its for a unified app like experience. It basically mimics the ChatGPT interface when it's all setup. You could use Ollama for the language model aspect but Ollama isnt going to generate images or respond with voice on it's own. You also can't link Ollama directly to a website withouy a front end.
Its to provide a complete experience thats useable from anywhere while storing data locally and it's free aside from having a web domain. It supports multiple users etc.