Llama.cpp fallback stabilizes local LLM setups

Using llama.cpp as a "safety net" is a pragmatic move for local inference, but it highlights the ongoing fragmentation of the LLM backend ecosystem. While it solves immediate hardware headaches, the trade-offs in inference speed and feature parity remain significant hurdles for developers.

• –Native GGUF support in llama.cpp provides the most reliable path for heterogeneous hardware environments compared to more volatile backends like ExLlamaV2 or AutoGPTQ.
• –GPU-to-CPU offloading remains the primary point of failure; memory fragmentation and context-window-induced crashes are frequently cited as stability killers.
• –Recent Qwen-specific kernel optimizations (GDN kernels) in llama.cpp have narrowed the performance gap, making it a viable primary driver rather than just a fallback for modern models.
• –The shift toward "unified" setup scripts suggests a growing demand for a standard local "driver" layer that provides more granular control than high-level abstractions like Ollama.