arXiv (OAI Expanded)
open access
MobiBench: Benchmarking LLMs for On-Device Performance
Understanding how large language models (LLMs) perform under real-world resource constraints has become more crucial due to the growing demand for deploying LLMs on mobile and edge devices. While there are a number of execution frameworks for on-device inference, the evaluation of llama.cpp as the r
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
- TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference#189269
- PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling#987994
- SiDP: Memory-Efficient Data Parallelism for Offline LLM Inference#238580
- AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference#924677
- AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference#920443
Record · ID 1070968
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