TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs
📰 ArXiv cs.AI
Learn how TileFuse enables efficient quantized LLM inference on AMD NPUs with a fused mixed-precision kernel library, improving performance and energy efficiency
Action Steps
- Implement TileFuse library in your LLM inference pipeline to leverage mixed-precision quantization
- Use AMD NPUs to accelerate LLM inference with improved performance and energy efficiency
- Evaluate the effectiveness of TileFuse in reducing power consumption and thermal budgets
- Apply TileFuse to various LLM models and tasks to explore its versatility
- Compare the performance of TileFuse with other quantization formats, such as AWQ, on AMD NPUs
Who Needs to Know This
Machine learning engineers and researchers working on LLM inference on edge devices can benefit from this work, as it provides a solution to the challenges of deploying LLMs on client NPUs
Key Insight
💡 TileFuse provides a fused mixed-precision kernel library for efficient quantized LLM inference on AMD NPUs, addressing the challenges of practical LLM deployment on client NPUs
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🚀 TileFuse: Efficient quantized LLM inference on AMD NPUs with mixed-precision kernel library 🚀
Key Takeaways
Learn how TileFuse enables efficient quantized LLM inference on AMD NPUs with a fused mixed-precision kernel library, improving performance and energy efficiency
Full Article
Title: TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs
Abstract:
arXiv:2606.11357v1 Announce Type: cross Abstract: With the growing demand for on-device LLM inference, edge SoCs increasingly integrate NPUs to improve performance and energy efficiency under tight power and thermal budgets. However, practical LLM deployment on current client NPUs remains difficult: widely used quantization formats such as AWQ do not map cleanly onto many existing NPU software stacks, which are often proprietary and expose limited low-level control. In this work, we present \tex
Abstract:
arXiv:2606.11357v1 Announce Type: cross Abstract: With the growing demand for on-device LLM inference, edge SoCs increasingly integrate NPUs to improve performance and energy efficiency under tight power and thermal budgets. However, practical LLM deployment on current client NPUs remains difficult: widely used quantization formats such as AWQ do not map cleanly onto many existing NPU software stacks, which are often proprietary and expose limited low-level control. In this work, we present \tex
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