Multimodal AI Application Architecture Consultant

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.