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Ask HN: Who uses open LLMs and coding assistants locally? Share setup and laptop

330 points threeturn | 2 comments | 31 Oct 25 13:39 UTC | HN request time: 0.595s | source

Dear Hackers, I’m interested in your real-world workflows for using open-source LLMs and open-source coding assistants on your laptop (not just cloud/enterprise SaaS). Specifically:

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.

1. manishsharan ◴[31 Oct 25 15:59 UTC] No.45773567[source]▶
I am here to hear from folks running LLM on Framework desktop (128GB). Is it usable for agentic coding ?
replies(1): >>45774358 #
2. strangattractor ◴[31 Oct 25 17:15 UTC] No.45774358[source]▶
Just started going down that route myself. For the money it performs well and runs most of the models at reasonable speeds.

1. Thermal considerations are important due to throttling for thermal protection. Apple seems best at this but $$$$. The Framework (AMD) seems a reasonable compromise (you can have almost 3 for 1 Mini). Laptops will likely not perform as well. NVIDIA seems really bad at thermal/power considerations.

2. Memory model matters and AMD's APU design is an improvement. NVIDIA GPUs where designed for graphics but where better than CPUs for AI so they got used. Bespoke AI solutions will eventually dominate. That may or may not be NVIDIA in the future.

My primary interest is AI at the edge.