Best local LLM for OpenClaw on RTX 6000 Pro? Trying to reduce GPT/Claude token costs
I’m joining a university this fall as an engineering assistant professor, and I’m planning to start integrating OpenClaw into our research workflows. I’ve already been using agentic coding tools heavily for a while, but I want to move toward more capable autonomous systems for both research and development.
I’m trying to figure out what the best local LLM setup would be on an NVIDIA RTX 6000 Pro (96 GB), particularly for:
- coding / agentic engineering
- technical writing
For people already running local setups: what models are actually working well right now?
I’m especially curious about how current local models compare against Claude Opus 4.7 and GPT-5.5 (are they much worse or comparable).
I’m a heavy LLM user, enough that I burn through Cursor limits very quickly (my $60 subscription got exhausted within ~3 days, most of the times only Opus worked for my coding tasks). Because of that, I’m wondering whether investing in long-term local inference infrastructure makes more sense.