LoopLynx: Scalable Dataflow Architecture for High-Speed LLM Inference on FPGA

On April 13, 2025, Jianing Zheng and Gang Chen introduced LoopLynx, a novel scalable dataflow architecture that combines spatial and temporal designs to optimize FPGA usage for large language model inference. Their study shows that LoopLynx achieves a 2.52x speed-up in latency compared to NVIDIA’s A100 while using only 48.1% of the energy, highlighting its potential for efficient and scalable LLM processing.

LoopLynx is a scalable dataflow architecture for efficient large language model inference, optimized for FPGA through a hybrid spatial-temporal design. It implements computationally intensive operators as large kernels to achieve high throughput while reusing them temporally to enhance performance. A multi-FPGA distributed architecture overlaps and hides data transfers, enabling full utilization of accelerators and scaling for model parallelism. Evaluations show LoopLynx matches state-of-the-art single FPGA performance and delivers a 2.52x speed-up over A100 with 48.1% energy consumption in dual-FPGA configuration.

NVIDIA’s research explores using Field-Programmable Gate Arrays (FPGAs) as an alternative to Graphics Processing Units (GPUs) for optimizing large language models. While GPUs have traditionally dominated LLM processing due to their parallel capabilities, FPGAs offer unique advantages through their post-manufacturing reprogrammability. The study utilized AMD’s Alveo U50 FPGA cards to implement transformer architectures with post-training quantization techniques, which reduce bit-width computations while maintaining model accuracy.

Researchers systematically mapped transformer models to the FPGA hardware, leveraging its reconfigurable nature. The findings revealed that FPGAs demonstrated superior energy efficiency for certain workloads, particularly with quantized models, while offering potential for multi-FPGA scaling and reduced latency through custom configurations.

This research suggests FPGAs could complement GPUs by giving organizations options to optimize hardware based on specific needs like energy consumption or real-time performance requirements. Although GPUs remain the dominant solution for AI workloads, NVIDIA’s exploration of alternative architectures demonstrates their commitment to innovation in the evolving AI landscape and highlights the importance of hardware diversity through collaboration between manufacturers and AI developers.