this post was submitted on 16 Jan 2026
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I'd like to set up a local coding assistant so that I can stop using Google to ask complex questions to for search results.

I really don't know what I'm doing or if there's anything that's available that respects privacy. I don't necessarily trust search results for this kind of query either.

I want to run it on my desktop, Ryzen 7 5800xt + Radeon RX 6950xt + 32gb of RAM. I don't need or expect data center performance out of this thing. I'm also a strict Sublime user so I'd like to avoid VS Code suggestions as much as possible.

My coding laptop is an oooooold MacBook Air so I'd like something that can be ran on my desktop and used from my laptop if possible. No remote access needed, just to use from the same home network.

Something like LM Studio and Qwen sounds like it's what I'm looking for, but since I'm unfamiliar with what exists I figured I would ask for Lemmy's opinion.

Is LM Studio + Qwen a good combo for my needs? Are there alternatives?

I'm on Lemmy Connect and can't see comments from other instances when I'm logged in, but to whomever melted down from this question your relief is in my very first sentence:

to ask complex questions to for search results.

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[–] Coolcoder360@lemmy.world 20 points 5 months ago

I've not found them useful yet for more than basic things. I tried Ollama, it let's you run locally, has simple setup, stays out of the way.

[–] ryokimball@infosec.pub 12 points 5 months ago (1 children)

I have heard good things about LM Studio from several professional coders and tinkers alike. Not tried it myself yet though, but I might have to bite the bullet because I can't seem to get ollama to perform how I want.

TabbyML is another thing to try.

[–] wasp_eggs@midwest.social 2 points 5 months ago (1 children)

Thanks for the reply!

I had noticed TabbyML but something about their wording made me rethink and then the next day I saw a post on here regarding the same phrasing, I decided to leave it alone after that

[–] scrubbles@poptalk.scrubbles.tech 5 points 5 months ago

Yeah I tried tabby too and they had like a mandatory "we share your code " line and I hoped out. Like if you're going to do that I might as well just use claude

[–] TomAwezome@lemmy.world 9 points 5 months ago

I get good mileage out of the Jan client and Void editor, various models will work but Jan-4B tends to do OK, maybe a Meta-Llama model could do alright too. The Jan client has settings where you can start up a local OpenAI-compatible server, and Void can be configured to point to that localhost URL+port and specific models. If you want to go the extra mile for privacy and you're on a Linux distro, install firejail from your package manager and run both Void and Jan inside the same namespace with outside networking disabled so it only can talk on localhost. E.g.: firejail --noprofile --net=none --name=nameGoesHere Jan and firejail --noprofile --net=none --join=nameGoesHere void, where one of them sets up the namespace (--name=) and the other one joins the namespace (--join=)

[–] perry@aussie.zone 8 points 5 months ago (3 children)

Qwen coder model from Huggingface, following the instructions there to run it in llama.cpp. Once that’s up: OpenCode and use the custom OpenAI API to connect it.

You’ll get far better results than trying to use other local options out of the box.

There may be better models potentially but I’ve found Qwen 2.5 etc to be pretty fantastic overall, and definitely a fine option beside Claude/ChatGPT/Gemini. I’ve tested the lot and it’s usually far more down to instruction and AGENTS.md instructions/layout than it is down to just the model.

[–] madcaesar@lemmy.world 3 points 5 months ago

Do you mind sharing your agents md?

[–] 70k32@sh.itjust.works 1 points 5 months ago

This. Llama.cpp with Vulkan backend running in docker-compose, some Qwen3-Coder quantization from huggingface and pointing Opencode to that local setup with a OpenAI-compatible is working great for me.

[–] melfie@lemy.lol 1 points 5 months ago* (last edited 5 months ago)

The main thing that has stopped me from running models like this so far is VRAM. My server has a RTX 4060 with 8GB, and not sure that can reasonably run a model like this.

Edit:

This calculator seems pretty useful: https://apxml.com/tools/vram-calculator

According to this, I can run Qwen3 14B with 4B quant and 15-20% CPU/NVMe offloading and get 41 tokens / s. It seems 4B quant reduces accuracy by 5-15%.

The calculator even says I can run the flagship model with 100% NVMe offloading and get 4 tokens / s.

I didn’t realize NVMe offloading was even a thing and not sure if it actually is supported or works well in practice. If so, it’s a game changer.

Edit:

The llama.cpp docs do mention that models are memory mapped by default and loaded into memory as needed. Not sure if that means that a MoE model like qwen3 235b can run with 8GB of VRAM and 16GB of RAM, albeit at a speed that is an order of magnitude slower like the calculator suggests is possible.

[–] hummingbird@lemmy.world 8 points 5 months ago

LM Studio in combination with Kilo Code for IDE integration works pretty nicely locally. Here is a good video covering the basics to get you going: https://www.youtube.com/watch?v=rp5EwOogWEw

[–] herseycokguzelolacak@lemmy.ml 5 points 5 months ago (1 children)

I recommend llama.cpp instead of LM Studio.

[–] surewhynotlem@lemmy.world 1 points 2 months ago (1 children)

Why? I use LM studio today, but I'm always interested in futzing with things. Is there a good reason to switch?

[–] killamon@piefed.zip 2 points 2 months ago

Llama.cpp is quite a bit faster than lm studio and ollama. It's easy to find benchmarks showing 2-3x speed ups. I recently switched and am liking it.

[–] neoscaler@feddit.org 1 points 5 months ago

I use Ollama with qwen-coder-2.5, integrated with Cline in VSCodium. Works great.

[–] Decronym@lemmy.decronym.xyz 1 points 2 months ago* (last edited 2 months ago)

Acronyms, initialisms, abbreviations, contractions, and other phrases which expand to something larger, that I've seen in this thread:

Fewer Letters More Letters
DNS Domain Name Service/System
Git Popular version control system, primarily for code
IoT Internet of Things for device controllers
NAS Network-Attached Storage
NVMe Non-Volatile Memory Express interface for mass storage
Plex Brand of media server package
SAN Storage Area Network
SBC Single-Board Computer
VPS Virtual Private Server (opposed to shared hosting)

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