Posted by threeturn 10/31/2025
Ask HN: Who uses open LLMs and coding assistants locally? Share setup and laptop
Which model(s) are you running (e.g., Ollama, LM Studio, or others) and which open-source coding assistant/integration (for example, a VS Code plugin) you’re using?
What laptop hardware do you have (CPU, GPU/NPU, memory, whether discrete GPU or integrated, OS) and how it performs for your workflow?
What kinds of tasks you use it for (code completion, refactoring, debugging, code review) and how reliable it is (what works well / where it falls short).
I'm conducting my own investigation, which I will be happy to share as well when over.
Thanks! Andrea.
It’s not very fast, and I built it up slowly without knowing quite where I was headed. If I could do it over again, I’d go with a recent EPYC with 12 channels of DDR5 and pair it with a single RTX 6000 Pro Blackwell.
On an RTX 3080 Ti+Ryzen 9
Assuming you ran inference for the full working day, you'd need to run your H200 for almost 2 years to break even. Realistically you don't run inference full time so you'll never realise the value of the card before it's obsolete.
That said, maybe a quantized version of GLM 4.5 Air, but if we're talking no hardware constraints I find some of the responses from LongCat-Chat-Flash to be favorable over Sonnet when playing around with LMArena.
I played around with renting H200s and coding with aider and gpt-oss 120b. It was impressive but not at the level of claude. I decided buying $30k worth of tokens made far more sense than buying 30k worth of one GPU.
- auto git commit message
- auto jira ticket creation from git diff
Tools: LM Studio for playing around with models, the ones I stabilize on for work go into ollama.
Models: Qwen3 Coder 30b is the one I come back to most for coding tasks. It is decent in isolation but not so much at the multi-step, context-heavy agentic work that the hosted frontier models are pushing forward. Which is understandable.
I've found the smaller models (the 7B Qwen coder models, gpt-oss-20B, gemma-7b) extremely useful given they respond so fast (~80t/s for gpt-oss-20B on the above hardware), making them faster to get to an answer than Googling or asking ChatGPT (and fast to see if they're failing to answer so I can move on to something else).
Use cases: Mostly small one-off questions (like 'what is the syntax for X SQL feature on Postgres', 'write a short python script that does Y') where the response comes back quicker than Google, ChatGPT, or even trying to remember it myself.
Doing some coding with Aider and a VS Code plugin (kinda clunky integration), but I quickly end up escalating anything hard to hosted frontier models (Anthropic, OpenAI via their clis or Cursor). I often hit usage limits on the hosted models so it's nice to have a way my dumbest questions don't burn tokens I want to reserve for real work.
Small LLM scripting tasks with dspy (simple categorization, CSV munging type tasks), sometimes larger RAG/agent type things with LangChain but it's a lot of overhead for personal scripts.
My company is building a software product that heavily utilizes LLMs so I often point my local dev environment at my local model (whatever's loaded, usually one of the 7B models), initially I did this not to incur costs but as prices have come down it's now more as it's less latency and I can test interface changes etc faster - especially as new thinking models can take a long time to respond.
It is also helpful to try and build LLM functions that work with small models as it means they run efficiently and portably on larger ones. One technical debt trap I have noticed with building for LLMs is that as large models get better you can get away with stuffing them with crap and still getting good results... up until you don't.
It's remarkable how fast things are moving in the local LLM world, right now the Qwen/gpt-oss models "feel" like gpt-3.5-turbo did a couple of years back which is remarkable given how groundbreaking (and expensive to train) 3.5 was and now you can get similar results on sub-$2k consumer hardware.
However, its very much still in the "tinkerer" phase where it's overall a net productivity loss (and massive financial loss) vs just paying $20/mo for a hosted frontier model.
What coding tasks do you use Qwen3 Coder 30b model for? Simple function definitions and / or as autocomplete in VSC?