Author(s): Addepalle Nikhil Varma

Originally published on Towards AI.

How I routed specialized tasks between 3 quantized SLMs to beat GPT-4o at coding tasks for exactly $0.00.

Ever opened your OpenAI dashboard at 2 AM and felt a sudden chill in your chest?

The rest of the article explains why typical “multi-agent frameworks” fail on consumer hardware—especially when multiple LLM contexts run concurrently—then presents a local, cost-free alternative that routes work sequentially across three specialized tiers: a lightweight pinned router for intent classification, a quantized code-focused worker for implementation, and a deterministic AST-based “compiler gate” that replaces unreliable LLM code review. It provides a minimal local orchestration blueprint (using Ollama-style local calls) with an unload/keep_alive strategy to keep RAM use low, shows what you should observe during a run (brief ~5GB peak with full AST verification), and backs the approach with benchmark-style comparisons on accuracy, cost, and latency versus cloud GPT-4o agents. Finally, it argues that edge-based orchestration and quantized models can turn a laptop into a self-contained “software factory,” avoiding recurring cloud rent and rate-limit risk.

Read the full blog for free on Medium.

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Published via Towards AI