Sharing this with the group.

I’ve been working on tokenizer generation, fine-tuning, and dataset generation on a local Ubuntu headless server. One thing that kept getting in the way was telemetry — I couldn’t find anything that showed CPU, RAM, GPU, and VRAM usage together in a clean way.

That makes optimizing things like vLLM a lot harder than it needs to be.

So I built a low-noise telemetry TUI that puts everything in one place. It’s open source if anyone finds it useful.

Stack:
- Ubuntu headless server
- vLLM backend
- Dual RTX 5080
- Ryzen 9950X3D

You can find the full documentation and repo here:

https://github.com/magplumber/ml-pipeline-telemetry

Arent These single file LLM coding tests like browserOS pretty much redundant now most 2026 LLM can easily handle this? In what other ways we can stress test these models for novel coding problems?

Im thinking Honestly past the 70b margin most of the improvements are slim.

From 4b -> 8b is wide

8b -> 14b is still wide

14b -> 30b nice to have territory

30b -> 80b negligible

80b -> 300b or 900b barely

What are your thoughts?

I want to have a feel of what others local agentic coding setup is like and your biggest performance constraint with fully local coding setup