Scaling Enterprise RAG with Accelerated Ethernet Networking and Networked Storage

Scaling Enterprise RAG with Accelerated Ethernet Networking and Networked Storage: A New Era in AI Performance

Summary: Retrieval-augmented generation (RAG) is a groundbreaking approach that addresses the limitations of large language models (LLMs) by augmenting queries with enterprise-specific information. Scaling RAG requires efficient data ingestion, low-latency responses, and robust networking. This article explores how accelerated Ethernet networking and network-connected storage can empower enterprises to develop scalable and efficient RAG-powered applications.

The Challenge of Scaling RAG

RAG is a powerful tool for enterprises looking to leverage AI for content generation, decision-making, and data analysis. However, scaling RAG to handle massive amounts of data and users poses significant technical challenges. The primary hurdles include:

• Data Ingestion: Efficiently transforming vast amounts of unstructured and structured data into actionable insights.
• Low-Latency Responses: Ensuring that RAG applications can deliver high-quality, relevant content in real-time.
• Networking: Providing robust, performant, and secure connectivity to support data ingestion and processing.

The Role of Accelerated Ethernet Networking

Accelerated Ethernet networking plays a crucial role in addressing these challenges. By leveraging high-speed Ethernet connections, enterprises can:

• Boost Data Transfer Speeds: Achieve incredible data transfer speeds of up to 100 Gbps, reducing the time it takes to process large datasets.
• Enhance Data Quality: Ensure high-quality data transfer without noise, using protocols like PAM3 (Pulse Amplitude Modulation) to reduce signal-to-noise ratios.
• Improve Power Efficiency: Benefit from lower energy consumption, thanks to Energy Efficient Ethernet (EEE) standards that put transmission circuits into low power mode when idle.

The Benefits of Network-Connected Storage

Network-connected storage is a game-changer for RAG-powered applications. By enabling access to blocks, files, and objects over a network, enterprises can:

• Scale Storage Capacity: Easily expand storage capacity by adding more disks or devices without affecting performance or data availability.
• Support Real-Time Streaming: Ingest real-time streaming data from various sources, such as social media, web, sensors, or IoT devices.
• Improve Metadata Annotation: Enhance data processing with metadata annotation, making it easier to generate relevant and up-to-date content.

Benchmarking RAG Performance

To demonstrate the performance advantages of accelerated Ethernet networking and network-connected storage, we conducted a series of benchmarks using NVIDIA GPU computing, NeMo Retriever microservices, and Amazon S3 object storage protocols.

Single-Node Performance

Our single-node test setup consisted of a DGX system with 8x A100 GPUs, connected to network-connected storage through an NVIDIA ConnectX-7 NIC. The results showed that using network-connected storage accelerated data ingestion by 36% compared to directly attached storage (DAS), reducing processing time by 122 seconds.

Multi-Node Performance

Our multi-node test setup used a distributed microservices architecture connected through NVIDIA BlueField-3 DPUs. The results demonstrated that multi-node GPU acceleration combined with network-connected storage delivered faster performance than using a single node, reducing processing time by almost 102 seconds.

Key Takeaways

• Accelerated Ethernet Networking: Boosts data transfer speeds, enhances data quality, and improves power efficiency.
• Network-Connected Storage: Scales storage capacity, supports real-time streaming, and improves metadata annotation.
• Benchmark Results: Demonstrate the performance advantages of accelerated Ethernet networking and network-connected storage in RAG-powered applications.

Future Directions

As RAG continues to expand to support new modalities such as video, data processing needs will continue to grow rapidly. NVIDIA generative AI microservices, combined with multi-node NVIDIA GPU compute inference, accelerated Ethernet networking, and network-connected storage, will play a crucial role in enabling efficient RAG inferencing at enterprise scale.

Table: Comparison of Single-Node and Multi-Node Performance

Table: Benefits of Network-Connected Storage

Conclusion

Scaling enterprise RAG requires a combination of efficient data ingestion, low-latency responses, and robust networking. Accelerated Ethernet networking and network-connected storage are essential components in achieving this goal. By leveraging these technologies, enterprises can develop scalable and efficient RAG-powered applications that deliver high-quality, relevant content in real-time.