Efficient On-Device Diffusion LLM Inference with Mobile NPU
This paper presents llada.cpp, the first NPU-aware inference framework for accelerating diffusion LLMs on smartphones. It introduces three techniques—Multi-Block Speculative Decoding, Dual-Path Progressive Revision, and Swap-Optimized Memory Runtime—to overcome challenges like shrinking workloads, KV cache reuse, and memory overhead. llada.cpp reduces LLaDA-8B generation latency by 17x-42x over CPU baseline with no quality loss.
[2606.13740] Efficient On-Device Diffusion LLM Inference with Mobile NPU
[Submitted on 11 Jun 2026]
Title:Efficient On-Device Diffusion LLM Inference with Mobile NPU
View a PDF of the paper titled Efficient On-Device Diffusion LLM Inference with Mobile NPU, by Tuowei Wang and 2 other authors
View PDF HTML (experimental)
Abstract:Diffusion large language models (dLLMs) accelerate generation by denoising multiple tokens in parallel, making them attractive for latency-sensitive mobile inference. However, repeated denoising introduces substantial computation on smartphones. Mobile neural processing units (NPUs) offer high-throughput dense matrix computation, but efficiently exploiting them remains challenging: token commitment shrinks per-block effective workloads, token revision complicates KV cache reuse, and limited NPU-visible address space incurs costly remapping and data transfer overheads.
In this paper, we propose this http URL, the first NPU-aware inference framework for accelerating dLLMs on smartphones. this http URL aligns block-wise dLLM inference with the execution characteristics of mobile NPUs through three techniques. (1) Multi-Block Speculative Decoding fills the shrinking workload in late-stage current-block decoding with speculative future-block tokens. (2) Dual-Path Progressive Revision keeps committed tokens revisable until stable and refreshes unstable tokens through a CPU-side path without stalling dense NPU execution. (3) Swap-Optimized Memory Runtime compacts NPU-visible address layouts and overlaps data staging with NPU computation to reduce remapping and transfer overheads. We implement this http URL as an end-to-end framework and evaluate it across diverse hardware platforms and dLLM workloads. this http URL reduces LLaDA-8B generation latency by 17x-42x over the CPU baseline with prefix KV cache reuse, while preserving generation quality.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.13740 [cs.LG]
(or arXiv:2606.13740v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.13740
arXiv-issued DOI via DataCite
Submission history
From: Tuowei Wang [view email] [v1] Thu, 11 Jun 2026 12:44:57 UTC (547 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Efficient On-Device Diffusion LLM Inference with Mobile NPU, by Tuowei Wang and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)