GPU Accelerated 5G RAN: Accelerating 5G SA L1 processing with the GPU

As 5G networks evolve towards open, cloud-native architectures on commercial off-the-shelf (COTS) hardware, a critical challenge has emerged: the performance bottleneck in the Distributed Unit (DU). The real-time, computation-intensive L1processing required for 5G is pushing traditional CPUs to their limits, threatening to cap network capacity and drive up operational costs.

But what if you could shatter these limits? What if you could unlock massive performance gains and dramatically improve energy efficiency using the same hardware that powers the AI revolution?

GlobalLogic embarked on a practical, hands-on study to answer that very question. We didn’t just theorize—we implemented, tested, and benchmarked a GPU-accelerated 5G RAN solution. This whitepaper contains the full results of our in-depth research.

What’s Inside: A Deep-Dive Practical Proof of Concept 

This whitepaper goes beyond theory to provide a transparent look at our implementation. Discover how our engineers ported essential 5G physical layer functions from the openairinterface5g stack to run on high-performance NVIDIA GPUs, leveraging the CUDA toolkit.

We put our solution through rigorous testing, focusing on two of the most critical 5G channels:

• PRACH (Physical Random Access Channel): The essential uplink channel for network access and synchronization.
• PDSCH (Physical Downlink Shared Channel): The crucial pipeline for all downlink user data.

Our benchmarks were conducted on both consumer-grade (NVIDIA RTX 4070) and industrial-grade (NVIDIA L40S) hardware, providing a realistic view of performance across different deployment scenarios.

Download the Whitepaper to Gain a Competitive Edge

The future of RAN is accelerated, intelligent, and efficient. GlobalLogic’s research demonstrates a clear and viable path to building next-generation vDUs that meet the demands of 5G and are ready for the AI-native era of 6G.

Download the  whitepaper to get the full technical breakdown, including:

• Detailed methodology for porting L1 code to CUDA.
• Complete benchmark data and comparison graphs.
• In-depth analysis of performance and energy consumption.
• Actionable insights into future-proofing your RAN infrastructure.