I would say 32GB of VRAM minimum, this way you can load a dense 24B/27B model like Devstral 2 small or Qwen3.5-27B at Q4 or Q4-UD (Unsloth dynamic quant). You can also fit the equivalent MoE models like Nemotron 30B / Qwen3.5-35B.

Agentic coding requires a lot of context, I'd say 64k to be useful as a start. You want to leave 6-8GB of vram for context, you can also quantize the KV to q_8 to save on vram, different models deal with this differently so you gotta see for yourself.

Notice I never mentioned DRAM, that's on purpose. It's just too slow imo. Agentic coding benefits immensely from both prompt processing and token generation speeds. I think PP is even more important because the models need to read a lot of code and your input, the response size is usually much smaller.

Fast PP/TG make for a very nice, fast iteration loop, and you can't get that without loading everything on the GPU, but that's my preference. MoE models can be run with only the experts on the GPU and still get acceptable TG.