I'm a noob to local LLMs. I want to use an LLM to create Python and Bash functions from well-defined specs written by me. I've used Claude's Sonnet for this mostly.

I'm a light user of LLMs and almost never hit the request limit on Claude.

It seems like cloud LLMs are in a price war right now and last-gen capabilities are bottoming out in cost? Is that correct?

I pay about $0.12 per kWh, probably going up as more AI data centers get built.

Hardware I have:

  • <16GB VRAM - AMD BC-250 APU - "16 GB total with approximately 12 GiB assigned to GPU UMA and 4 GiB left for the OS" (hardware unlocking changes by the day)
  • 8GB RAM - 11th gen Intel laptop with Xe graphics
  • 8GB RAM - Pixel 7a
  • 32 GB DDR3 - 2nd gen intel - doubt this does anything

Hardware I'm eventually selling:

  • 8GB VRAM RTX 3070 + 32 GB DDR5 + 1TB NVMe SSD - AMD Ryzen 5 7600X CPU - putting this here in case it's substantially better than the BC-250

Among this hardware, I should look for a model that fits on the BC-250?

you are viewing a single comment's thread
view the rest of the comments
[–] 2 points 1 month ago

I can second Ornith! I have an AMD 6650xt 8gb (it's rdna2 so I can't take advantage of rocm and have to use vulkan).

Ornith is absolutely the best code assist model I've found in the small local space so far. It provides usable output and is really good with tool calling.

The key for getting a model running well locally is not just the model, but figuring out exactly what arguments you should use with llamacpp to run it well on your machine. Since I'm on a dedicated GPU, I need to make sure the full context and kv cache fit fully in VRAM and morning gets offloaded. Offloading to CPU and RAM is the biggest hindrance to token throughput that I've found.

  • source
  • parent