AI Inference Guide
Core Concepts
Deployment Options
Tools & Services
Advanced Topics
Vision Language Models
VLM Edge Inference
Specialized solutions for running Vision Language Models on edge devices, optimized for real-time applications like autonomous driving, robotics, and mobile vision tasks.
Key Features
Key Features
VLM Optimization Techniques
Patch Selection
Filter irrelevant camera views to reduce computational overhead
Token Selection
Reduce input sequence length for the language model component
Speculative Decoding
Accelerate token generation with predictive techniques
FP8 Quantization
Further reduce model size and increase inference speed
Vision Language Models
VLM Edge Inference
Specialized solutions for running Vision Language Models on edge devices, optimized for real-time applications like autonomous driving, robotics, and mobile vision tasks.
Key Features
Key Features
VLM Optimization Techniques
Patch Selection
Filter irrelevant camera views to reduce computational overhead
Token Selection
Reduce input sequence length for the language model component
Speculative Decoding
Accelerate token generation with predictive techniques
FP8 Quantization
Further reduce model size and increase inference speed