AI Inference Guide
Vision Language Models
VLM Edge Inference
Research and runtime approaches for Vision Language Models on constrained devices. The linked LiteVLM and EdgeVLA results are research prototypes; reported benchmarks do not establish production readiness or functional-safety compliance for autonomous-driving or robotics systems.
Key Features
Paper-reported latency gains
Patch selection
Token optimization
FP8 quantization
Use Case: Autonomous driving
Key Features
Paper-reported speed gains
Small language models
Edge-focused evaluation
Memory-aware design
Use Case: Robotics
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
Can reduce memory use or improve throughput on supported hardware/runtimes; measure task-quality impact