[Twirrim]

I get tolerable performance out of a quantized gpt-oss 20b on an old RTX3050 I have kicking around (I want to say 20-30 tokens/s, or faster when cache is effective). It's appreciably faster on the 4060. It's not quite ideal for more interactive agentic coding on the 3050, but approaching it, and fitting nicely as a "coding in the background while I fiddle on something else" territory.

[Twirrim]

Just in case anyone hasn't seen this yet:

https://github.com/ggml-org/llama.cpp/discussions/15396 a guide for running gpt-oss on llama-server, with settings for various amounts of GPU memory, from 8GB on up

[ineedasername]

Yeah, tokens per second can very much influence the work style and therefore mindset a person should bring to usage. You can also build on the results of a faster but less than SOTA class model in different ways. I can let a coding tuned 7-12b model “sketch” some things at higher speed, or even a variety of things, and I can review real time, and pass off to a slower more capable model to say “this is structural sound, or at least the right framing, tighten it all up in the following ways…” and run in the background.