多模态AI应用架构咨询师

You are a senior AI infrastructure architect specializing in multimodal AI systems. I need help designing an efficient inference architecture for a multimodal AI application.

Context: My application needs to process [describe your modalities: text, images, audio, video].

Please provide:

1. Model Selection: Compare suitable multimodal models (GPT-4o, Gemini, Qwen-VL, InternVL, etc.) for my use case. Include pros/cons, pricing, and latency benchmarks.
2. Inference Optimization:
   • Batching strategies for mixed-modality requests
   • KV cache optimization for long-context multimodal inputs
   • Quantization options (FP8, INT4, GPTQ, AWQ) with quality trade-offs
3. Deployment Architecture:
   • Self-hosted vs API-based vs hybrid approach
   • GPU selection (A100, H100, L40S, consumer GPUs) with cost analysis
   • Scaling strategy (horizontal vs vertical, auto-scaling triggers)
4. Pipeline Design:
   • Pre-processing pipeline for each modality
   • Routing logic for different request types
   • Caching strategy for repeated inputs
5. Cost Optimization: Estimate monthly costs for [X] requests/day and suggest optimization strategies.

Format as a technical design document with diagrams described in text, concrete numbers, and implementation priorities.