Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects

This project is currently a research survey/taxonomy rather than a production-ready software artifact, as evidenced by its 3-day age and lack of stars despite some initial fork activity (likely researcher-driven). The core value lies in identifying 'visual token dominance'—a well-known bottleneck where high-resolution images generate thousands of tokens that choke transformer attention layers. While the taxonomy is valuable for researchers, it lacks a technical moat. Frontier labs (OpenAI, Google, Anthropic) are the primary innovators in this space; they are actively building internal, proprietary versions of these optimizations (e.g., GPT-4o's native multimodal tokenization). Competitive projects like vLLM and TensorRT-LLM already implement the low-level kernels required for these optimizations. The defensibility is low because the 'moat' is purely intellectual synthesis which is quickly commoditized as soon as the paper is read. Displacement risk is high because the field of LVLM efficiency moves at an extreme velocity; techniques discussed today (like simple token pruning) are often superseded by architectural changes (like native multimodal training) within months.