Qwen3.5 Small targets low-RAM self-hosting

Qwen3.5 Small looks like the right size class for hobbyist self-hosters, but it also shows the hidden tax of multimodal convenience: the model is tiny, yet the vision path can still dominate latency.

• –The official Qwen3.5-2B model card frames it as a 2B multimodal model with 262k context and non-thinking mode by default, which makes it attractive for lightweight local agents.
• –The user’s roughly 10 GB free RAM budget makes the 2B GGUF practical, especially for text-first jobs, but each image still adds encoder overhead that quantization alone cannot erase.
• –For tagging links or extracting ingredients, this size tier should be a strong fit; for Frigate alert summarization, batching, downscaling, or preprocessing images will matter more than squeezing a few more points out of the quant.
• –The bigger signal is that Qwen is pushing “good enough” multimodal capability into edge-friendly sizes, which should make private, self-hosted AI features easier to adopt.
• –This is an inference from the architecture rather than a measured benchmark claim, but the split vision/text path is exactly where the latency pain would be expected.