I ran my local LLM for hours and watched it get dumber in real time

Running a local LLM (Qwen 3.6 27B) on an RTX 5090 for extended periods causes noticeable performance degradation. The root causes are not model corruption or uptime, but two interacting issues: the KV cache pre-allocated for a 262K context window consumes enough VRAM to push total usage over 32GB, causing silent spillover into system RAM via PCIe; and as conversations grow, the context window fills up, causing the model to drop early instructions and lose conversational history. Qwen 3.6's hybrid architecture (only 16 of 64 layers use full attention) means KV cache is smaller than expected, but combined with model weights and OS overhead, VRAM still overflows. The fix is simple: start a new chat or reload the model to clear the cache. The model weights themselves never change during inference.

Nguồn: https://www.xda-developers.com/ran-my-local-llm-for-hours-and-watched-it-get-dumber-in-real-time. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.

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