User Praises Quantized Qwen 35B Model for Broad Agentic Home Automation Tasks
A local LLM user highlighted the practical effectiveness of Unsloth's dynamic GGUF quantization (UD_Q4_K_XL) of Qwen 35B for general agentic tasks and home automation rather than software development. Powered by dual Tesla P100 GPUs, the model achieves high generation speeds of 120–140 tokens per second while reliably executing multi-step actions such as SSH commands and automated reminders. The user experience highlights a growing demand for fast, general-purpose AI assistants capable of real-world ambient computing rather than hyper-specialized coding models. It demonstrates how dynamic quantization paired with legacy server GPUs can deliver conversational, agentic AI performance locally and affordably. Despite weaknesses in medium-to-large project coding, persistent persona adherence, and occasional hallucinations, the model excelled at execution, tool use, and sticking to tasks across local Tailscale network environments. It achieved prompt processing speeds of up to ~1000 tokens per second at 0 context and ~700 tokens per second at a 13k context window.
## BACKGROUND
Quantization reduces an LLM's memory footprint by converting network weights to lower bit precision, with Unsloth's Dynamic Quantization (UD_Q4_K_XL) retaining higher precision on sensitive tensors to preserve reasoning baseline. Agentic AI refers to systems where language models act autonomously to set sub-goals, use external tools, and manipulate external environments to complete multi-step tasks.