Local Models Just Crossed the "Good Enough" Line for Agentic Coding

Three signals hit in the same week. That's not coincidence, that's a threshold.

First: Qwen 3.6-35B-A3B is running at 44 tokens per second on a single 16GB GPU at Q4 quantization with 100K context. It's a 35B-parameter MoE model with only 3B active parameters per token. A 293-upvote r/LocalLLaMA thread has people saying it's replaced their cloud agent subscriptions for daily coding work.

Second: llama.cpp b9274, released May 21, fixed a critical VRAM leak in the Multi-Token Prediction stack. Speculative decoding resources weren't being freed during idle cycles, silently accumulating until your server crashed. If you tried running a local agent 24/7 and it died after a few hours, this was probably why. Fixed now.

Third: a 106-upvote thread mapped out the complete hardware path. An RTX 6000 or dual RTX 5090 setup running Qwen 3.6 through llama.cpp gets you roughly 80% of cloud agent quality for a one-time ~$20K spend. The community consensus is clear on one point: the model can code, but it can't self-verify. Reliability comes from wrapping it in structured loops. Git diff checks, test suites, file-allow gates.

This is the insight that matters. The agent loop is the product, not the model. If you design your workflow around external verification instead of trusting model self-assessment, a $20K local setup gets you surprisingly close to a $200/month cloud subscription. The economics flip around month ten.

I don't think local models replace cloud APIs for everything. Long-context planning, complex multi-file refactors, novel architecture decisions. Those still need frontier models. But for the 70% of coding work that's well-defined tasks with clear test coverage? Local is real now.