Core Goals
- Model capability retention: Multimodal understanding capability decline not exceeding baseline 3%
- Compression efficiency: Model size reduced by 50-75%
- Inference speedup: Achieve 2-4x inference speed improvement under typical batch size
Hardware Adaptation Matrix
| Hardware Type | Representative Models | Quantization Support Features |
|---|---|---|
| Consumer GPU | RTX 3090/4090 | INT8/FP16 mixed precision |
| Server-grade GPU | A100/H100 | FP8 precision format (H100) |
| Edge device | Jetson Orin | INT8 sparse quantization |
Candidate Models
Small Models (~3B parameters)
- Qwen2.5-VL-3B: Suitable for edge device deployment
Medium Models (6B~13B parameters)
- BLIP-2 (approximately 6B parameters)
- MiniGPT-4 Vicuna-13B
- LLaVA-13B
Large Models (70B+ parameters)
- Qwen2.5-VL-7B/72B
- OpenFlamingo-9B
- IDEFICS-80B
Quantization Scheme Comparison
- PTQ (Post-Training Quantization): GPTQ, SmoothQuant, Q-VLM
- QAT (Quantization-Aware Training): Compare when resources allow
- QLoRA: Evaluate performance recovery effect
Mixed Precision Strategies
- W8A8 (INT8 weights/activations)
- W8A16 (INT8 weights/FP16 activations)
- W4A8/W4A16 (INT4 weights)
Key Evaluation Metrics
1. Accuracy Retention
- VQA accuracy
- BLEU, CIDEr scores for image caption generation
- Recall@K for image-text retrieval
2. Model Compression
- Memory footprint (MB/GB)
- Compression ratio: 32bit→8bit (4x), 8bit→4bit (2x)
3. Inference Speed
- Latency (ms) and throughput (samples/sec)
- Single-stream low latency vs. batch high throughput scenarios
Test Datasets
- COCO Captions (caption generation)
- Flickr30k (image-text retrieval)
- VQA v2 (visual question answering)
- ActivityNet-QA (video question answering)
- DocVQA (document question answering)
Quantization Analysis Dimensions
Accuracy vs. Compression Ratio Curve Characteristics
- FP32→INT8: Accuracy loss typically <1%
- INT8→INT4: Loss may reach 5-10%
Quantization Robustness Factors
- Parameter count: 70B more resistant to quantization than 7B
- Attention heads: Multi-head attention has higher redundancy
- Activation functions: GELU more tolerant to quantization than ReLU
Recovery Methods Comparison
| Method | Data Volume | Training Time | Accuracy Recovery |
|---|---|---|---|
| Pure PTQ | 0 | 0 | Baseline |
| QAT (1%) | 1k samples | 2 hours | +3.2% |
| QLoRA (5%) | 5k samples | 8 hours | +5.7% |
Expected Milestones
- Phase 1 (1-2 weeks): ViT-B/16 visual encoder INT8 quantization verification
- Phase 2 (3-4 weeks): Cross-modal attention layer mixed precision quantization
- Phase 3 (5-8 weeks): End-to-end inference pipeline optimization