Efficient-LVLMs-Inference: A Comprehensive Analysis of Efficient Inference Techniques for Large Vision-Language Models

The Efficient-LVLMs-Inference project, based on the ACL 2026 Findings paper, focuses on the inference efficiency bottlenecks of large vision-language models (LVLMs), systematically organizes optimization techniques, and provides open-source resources. Through the "paper + code" model, the project offers a comprehensive reference for LVLM inference optimization, facilitating the deployment of multimodal AI.

This project is the official implementation repository of the ACL 2026 Findings paper titled "Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects". As a review study, the paper comprehensively analyzes LVLM inference bottlenecks and optimization techniques. The repository provides code, experiment reproduction, and literature tracking to ensure the verifiability and practicality of the results.

Inference Bottlenecks

1. Computational Bottleneck: Matrix operations of visual encoders and language decoders dominate, and high-resolution image processing consumes significant resources.
2. Memory Bottleneck: The large number of visual tokens leads to KV Cache usage far exceeding pure text scenarios, which is exacerbated by long dialogue problems.
3. Communication Bottleneck: In distributed deployment, visual feature transmission and multi-device coordination are prone to delays.

Optimization Technology Classification

• Model architecture optimization: Lightweight visual encoders, projection layer compression, multimodal attention improvement.
• Inference algorithm optimization: Dynamic batching, speculative decoding, early exit.
• Quantization compression: Visual feature quantization, weight-activation joint quantization, KV Cache quantization.
• System-level optimization: Efficient attention kernels, memory management, distributed frameworks.

Visual Token Compression

• Spatial downsampling: Reduce feature map resolution while preserving key details.
• Semantic aggregation: Intelligently merge similar regions, keeping high resolution for important areas.
• Token pruning: Remove low-impact tokens based on attention/gradient, with adaptive compression.

KV Cache Management

• Visual KV compression: Adopt aggressive compression (e.g., low-rank approximation) for static visual tokens.
• Cross-turn reuse: Reuse image KV across multiple dialogue turns to reduce latency.
• Hierarchical caching: Use different strategies based on token importance/frequency.

Hardware-aware Optimization

• GPU optimization: Tensor Core utilization, memory access optimization, custom CUDA kernels.
• Edge deployment: NAS-driven model design, hardware-software co-optimization.

Experimental Findings

• Visual token compression reduces computation by over 50% with minimal performance loss.
• 4-bit visual encoder + 8-bit language decoder achieves near-lossless quantization.
• System optimizations like FlashAttention are effective in LVLM scenarios (need to adapt to multimodality).

Practical Resources

• Code implementation: PyTorch implementations of mainstream optimization techniques.
• Reproduction scripts: Complete experiment configurations to support result reproduction.
• Literature library: Continuously updated paper list classified by technology.
• Performance benchmarks: Performance data of optimization techniques across multiple hardware platforms.

Community Significance

Establish a systematic knowledge framework, provide unified classification and benchmarks, avoid redundant work, and promote innovation in LVLM efficiency optimization.

Future Outlook

• Adaptive compression: Dynamic compression strategies combining tasks and inputs.
• End-to-end optimization: Joint design of visual and language modules.
• New hardware adaptation: Optimization for AI accelerators and in-memory computing chips.
• Long video extension: Handling temporal information and large computing demands.