arXiv Machine Learning
4d ago

Shared Context, Separate Engines: Streaming Vision--Language Inference on Edge SoCs

The paper introduces a system that coordinates heterogeneous accelerators on edge SoCs to run vision–language model inference concurrently, separating a stream’s context from each engine’s key‑value state. By allowing engines to import, reuse, or reconstruct state, the method supports stateful streaming generation and concurrent sequence generation, achieving higher throughput and lower latency than single‑engine baselines. Experiments on Apple M4 Max, Rockchip RK3588, and Qualcomm QCS6490 show significant gains, including up to 27.2% more throughput and 72.7% faster query completion.

By Yilong Li, Chengpo Yan, Yijing Zeng, Hanlei Wu, Mingjun Song, Suman Banerjee
arXiv AI
Sep 28

Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware

The paper reports a reproducible GPU power benchmark for 18 open‑source LLMs (0.5B–7B parameters) run on a single consumer RTX 4060ti GPU using the Ollama inference engine. Energy metrics such as mean/peak power, total energy per prompt, energy per output token, and throughput were measured, revealing that model architecture and quantization strategy, rather than parameter count alone, drive energy efficiency. The most efficient models were qwen2.5:0.5b and tinyllama:1.1b, while the 7B‑Mistral model consumed up to 8.6× more energy per token, and qwen3.5:0.8b(on) showed unusually high per‑prompt energy due to extended internal reasoning.

By Philipp M. Z\"ahl, Elja Dalipaj, Anika Hennig, Timon Bayer