Table of Contents

1. Introduction
2. Why Inference Matters at Scale
3. Dell + NVIDIA: A Powerhouse Duo
4. Spotlight: Dell HGX‑2 Platform
5. Case Study: AT&T Edge AI (Illustrative Architecture)
6. Scaling and Architecture Insights
7. Performance, Efficiency & Benchmarks
8. Deployment Best Practices
9. Conclusion

1. Introduction

In today’s enterprise landscape, AI inference—applying trained models in production—is mission-critical. Large-scale deployment demands low latency, high throughput, and seamless integration with data center and edge infrastructure. Dell and NVIDIA have joined forces to tackle these challenges head on.

2. Why Inference Matters at Scale

• Real-time impact: From real-time fraud detection to voice assistants, inference delivers instantaneous business value.
• Resource demand: CPUs alone can’t handle high-concurrency workloads; GPUs provide essential parallelism.
• Cost vs. performance: Efficient inference reduces latency and total cost of ownership.

3. Dell + NVIDIA: A Powerhouse Duo

Dell’s AI Factory with NVIDIA, unveiled May 19, 2025, emphasizes end-to-end AI solutions across compute, storage, networking, and software (dell.com).

• New hardware platforms: PowerEdge XE9780/XE9785 servers support up to 192 Blackwell Ultra GPUs; rack configurations support 256 GPUs.
• Networking & data: Quantum‑X800 InfiniBand and Spectrum‑X Ethernet with BlueField‑3 DPUs ensure low latency and high throughput. Storage stacks (ObjectScale, PowerScale + Project Lightning) optimize inferencing workloads.

4. Spotlight: Dell HGX‑2 Platform

Though the HGX‑2 itself predates the Blackwell generation, Dell’s HGX‑2-based PowerEdge servers (e.g., XE9680) remain powerful inference platforms, air or liquid cooled, with NVLink-enabled multi-GPU coherence. Enterprises can leverage HGX‑2 configurations to support a mix of AI workloads, combining inference and training at scale.

5. Case Study: AT&T Edge AI (Illustrative Architecture)

While AT&T is actively deploying AI at the edge, public sources do not confirm specific use of Dell HGX‑2 or B300 platforms at cell sites. The following architectural example is illustrative, reflecting what’s possible with Dell APEX MEC and NVIDIA enterprise AI solutions:

• AT&T’s Edge Vision:
  AT&T is expanding MEC (Multi-Access Edge Computing) to enable low-latency AI inference for real-time analytics—such as traffic management and pedestrian safety, directly at the network edge (delltechnologies.com).
• Technology Alignment:
  Dell’s PowerEdge servers, NVIDIA AI Enterprise stack (including NeMo, Riva, and NIM), and APEX MEC infrastructure deliver the scalability and reliability required for such workloads.
• Illustrative Architecture:
  • Dell PowerEdge chassis or APEX MEC appliances at edge sites
  • NVIDIA enterprise-class GPUs (e.g., A100, B100, or comparable)
  • 5G network backhaul
  • Real-time data feeds processed by AI models, with local inference before aggregating results to central operations
• Important Note:
  This architecture is technically feasible and aligns with Dell’s MEC offerings, but public details of exact hardware at AT&T’s cell sites remain undisclosed. Treat this as a representative design, not a confirmed deployment.

6. Scaling & Architecture Insights

Edge deployments mirror central infrastructure but optimized for ruggedization, compactness, and autonomy via Dell APEX MEC systems.

7. Performance, Efficiency & Benchmarks

• Dell HGX B300 (with Blackwell) delivers 4× faster LLM training versus previous generations.
• XE9680 + liquid cooling delivers 25× inferencing performance, 20× lower TCO & energy vs HGX H100.
• Storage enhancements: S3‑RDMA on ObjectScale increases throughput by ~230%, cuts latency by ~80%, and reduces CPU usage by ~98%.

8. Deployment Best Practices

• Right‑size GPU stack per workload: Balance GPU count vs power/cooling.
• Network offloading: Use BlueField DPUs to relieve CPU overhead.
• Efficient storage I/O: Adopt S3‑RDMA and KV-cache for high-eff inferencing.
• Software orchestration: NVIDIA’s AI Enterprise suite and Dell ProSupport streamline deployment.
• Managed Services: Outsourcing 24/7 monitoring, patching, and upgrades accelerates ROI.

9. Conclusion

Dell’s AI Factory powered by NVIDIA hardware and software, anchored by HGX‑2/HGX B300 and edge‑capable PowerEdge + APEX systems, enables enterprises and telcos to deploy inference workloads at unprecedented scale, speed, and efficiency. From data center racks to 5G edge sites, the platform delivers real-world impact, illustrated by AT&T’s MEC strategy and vision.

Disclaimer

AT&T is actively deploying AI at the edge, but the specific use of Dell HGX‑2 or B300 hardware at cell sites is not confirmed in public sources. The architecture described here is technically feasible and aligns with Dell’s APEX MEC offerings, but should be treated as illustrative.

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