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NITRO: LLM Inference on Intel Laptop NPUs

arXiv 2412.11053 ↗First posted 15 Dec 2024cs.CL cs.AIcited by 3

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Abstract (arXiv)

Large Language Models (LLMs) have become essential tools in natural language processing, finding large usage in chatbots such as ChatGPT and Gemini, and are a central area of research. A particular area of interest includes designing hardware specialized for these AI applications, with one such example being the neural processing unit (NPU). In 2023, Intel released the Intel Core Ultra processor with codename Meteor Lake, featuring a CPU, GPU, and NPU system-on-chip. However, official software support for the NPU through Intel's OpenVINO framework is limited to static model inference. The dynamic nature of autoregressive token generation in LLMs is therefore not supported out of the box. To address this shortcoming, we present NITRO (NPU Inference for Transformers Optimization), a Python-based framework built on top of OpenVINO to support text and chat generation on NPUs. In this paper, we discuss in detail the key modifications made to the transformer architecture to enable inference, some performance benchmarks, and future steps towards improving the package. The code repository for NITRO can be found here: https://github.com/abdelfattah-lab/nitro.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 15 Dec 2024.

Builds on

3 of 7 references.

References · 3

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  3. 2023Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Timecited by 252

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.