Qwen3.6 multimodal stripping sparks efficiency debate

This is less a hidden optimization trick than an architecture and packaging question.

• –In dense models, vision/audio front ends and projection layers can often be removed or bypassed, but the main transformer weights usually remain the dominant footprint.
• –In Qwen-style multimodal stacks, the multimodal parts are often separate encoders/adapters feeding a shared language core, so stripping them can save some VRAM and latency without making the model fundamentally smaller.
• –MoE shifts the tradeoff: fewer experts are active per token, so you can sometimes avoid loading or using modality-specific routes, but the routing layers and the full expert set still exist.
• –The big limiter is product strategy, not feasibility: vendors usually prefer one checkpoint that covers text, image, and audio rather than maintaining a patched text-only fork.
• –If the goal is real speed or size reduction, a native text-only model or a separately trained text variant is usually cleaner than trying to amputate multimodal ability after the fact.