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NITRO: LLM Inference on Intel Laptop NPUs

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arxiv 2412.11053 v1 pith:QJGPYMAC submitted 2024-12-15 cs.CL cs.AI

classification cs.CLcs.AI
keywords inferenceintelnitroareaframeworkgenerationlanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU

    cs.DC 2026-07 conditional novelty 6.0 of 10

    STEEL maps fused FlashAttention onto XDNA NPUs with sparsity-aware pipeline placement, cutting energy ~9 imes vs CPU and ~1.75 imes vs GPU and beating prior XDNA attention by ~9.6× latency.

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