Principle:Mit han lab Llm awq VLM Benchmarking
| Knowledge Sources | |
|---|---|
| Domains | Benchmarking, Multimodal |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Principle of evaluating vision-language model performance across standardized multimodal tasks with timing measurements.
Description
VLM benchmarking evaluates quantized multimodal models on four standard tasks: image captioning, image question answering, video captioning, and video question answering. Each task uses predefined prompts and measures inference latency including vision encoding time and language generation time. The benchmark reports tokens per second and end-to-end latency, enabling comparison of different quantization configurations.
Usage
Apply this principle when evaluating the speed and quality tradeoffs of different quantization settings for multimodal models.
Theoretical Basis
Benchmark metrics include:
- Prefill latency: Time to encode visual features and process the prompt
- Decode throughput: Tokens generated per second during autoregressive generation
- End-to-end latency: Total time from input to complete response
Related Pages
Page Connections
Double-click a node to navigate. Hold to expand connections.
Principle
Implementation
Heuristic
Environment