arXiv (All)
open access
Seeing at the Edge: Energy Characterization of Vision-Language Model Inference on Edge Platforms
Vision-language models (VLMs) are moving onto edge devices, but little is known about how much energy they draw on single-board computers with NPU or GPU accelerators, or where that energy goes. We measure total-system energy, latency, and thermal behavior of Qwen2-VL-2B-Instruct and its text-only s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism#1344526
- BareBones: Benchmarking Zero-Shot Geometric Comprehension in VLMs#1216278
- DVFS for Small Language Model Inference on Mobile Edge Devices#1070962
- FLIP: Final Layer Inference-Time Probing for Vision-Language Models#1105552
- Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation#1144989
Record · ID 1355071
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive