I was only using a single DGX Spark, and this was earlier in the year, so I was running some pretty aggressively quantized models — probably in the 1–3 bit rang

I was only using a single DGX Spark, and this was earlier in the year, so I was running some pretty aggressively quantized models — probably in the 1–3 bit range. My main issue at the time was that my financial data had lots of messy notes, comments, and irregular annotations. The quantized models often failed to process all of that context consistently and would miss things. So I ended up generating a fake dataset with the same structure, asking Claude Code to work out the analysis on that, and then bringing the result back to the local model for the final pass. I was mainly using llama.cpp at the time, before B12X support was integrated into vLLM, so I think I wasn't using it then.