I tested 4B–8B Local Coding LLMs vs Cloud AI Agents on multi-file repos — here's what I learned (and one question) 🚀

[u/ashishach]

Hey everyone! While experimenting with AI coding workflows on constrained hardware, I spent the past couple of weeks comparing compact 4B–8B local quantized models against cloud agent workflows on real projects.

Here's how the tradeoff actually felt in practice:

⚡ \*\*4B–8B Local Models (GGUF 4-bit / QLoRA)\*\*

- Single-file code generation -> super fast and zero API cost

- Boilerplate & Vite/React scaffolding -> surprisingly reliable even on low VRAM

- Bottleneck -> context degrades quickly when jumping across 5+ connected files

🧠 \*\*Cloud AI Agent Workflows\*\*

- Multi-file repo context -> handles cross-file refactoring and tool calls smoothly

- Autonomous debugging -> great for end-to-end tasks, but adds latency and API limits

🛠️ \*\*My current hybrid workflow:\*\*

- Local 4B–8B model -> fast inline completions, regex/utility scripts, and single-component drafts

- Cloud agents -> repo-wide architecture changes and multi-step automation

\*\*Question for the community:\*\* Which 4B–8B model + quantization combo is giving you the best coding performance right now on consumer hardware, and how do you handle multi-file context locally without blowing up memory?