mlsanityData to 14 Oct 2026

iOS as Acceleration

arXiv 2512.22180 ↗First posted 19 Dec 2025cs.DC cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Practical utilization of large-scale machine learning requires a powerful compute setup, a necessity which poses a significant barrier to engagement with such artificial intelligence in more restricted system environments. While cloud computing offers a solution to weaker local environments, certain situations like training involving private or sensitive data, physical environments not available through the cloud, or higher anticipated usage costs, necessitate computing locally. We explore the potential to improve weaker local compute systems at zero additional cost by taking advantage of ubiquitous yet underutilized resources: mobile phones. Specifically, recent iOS phones are equipped with surprisingly powerful processors, but they also face limitations like memory constraints, thermal throttling, and OS sandboxing. We present a proof-of-concept system demonstrating a novel approach to harness an iOS device via distributed pipeline parallelism, achieving significant benefits in a lesser compute environment by accelerating modest model training, batch inference, and agentic LRM tool-usage. We discuss practical use-cases, limitations, and directions for future work. The findings of this paper highlight the potential for the improving commonplace mobile devices to provide greater contributions to machine learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 56 references that are in the map. First posted on arXiv 19 Dec 2025.

Builds on

7 of 56 references.

References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  4. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2018PipeDream: Fast and Efficient Pipeline Parallel DNN Trainingcited by 117
  7. 2024Understanding Large Language Models in Your Pockets: Performance Study on COTS Mobile Devicescited by 10

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices
  2. 2026Memory-Efficient Partitioned DNN Inference on Resource-Constrained Android Crowds
  3. 2022Benchmarking of DL Libraries and Models on Mobile Devices
  4. 2024Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
  5. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
  6. 2023A Performance Evaluation of a Quantized Large Language Model on Various Smartphones
  7. 2025Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference
  8. 2024Distributed Convolutional Neural Network Training on Mobile and Edge Clusters