Escaping API Quotas: How I Built a Local 14B Multi-Agent Squad for 16GB VRAM (Qwen3.5 & DeepSeek-R1)

I was building a complex web app prototype using a cloud-based AI IDE. Just as I was getting into the flow, I hit the dreaded wall: "429 Too Many Requests". I was done dealing with subscription anxiety and 6-day quota limits. I wanted to offload the heavy cognitive work to my local machine. But there was a catch: my rig runs on an AMD Radeon RX 6800 with 16GB of VRAM. Here is how I bypassed the cloud limits and built a fully functional local multi-agent system without melting my GPU. The "Goldilocks" Zone: Why 14B? Running a multi-agent system locally is tricky when you have strict hardware limits. Through trial and error, I quickly realized: 7B/8B models? They are fast, but too prone to hallucination when executing complex MCP (Model Context Protocol) tool calls or strict JSON outputs. 32B+ models? Immediate Out Of Memory (OOM) on 16GB VRAM. I found the absolute sweet spot: 14B models quantized (GGUF Q4/Q6) via Ollama. They are smart enough to reliably follow system prompts and handle