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As AI adoption accelerates across industries, the choice of hardware becomes critical in optimising performance and efficiency. While NVIDIA AI accelerators like the H100, H200, and the recently announced B200 are leading the charge in AI workloads, their performance is not determined by the GPU alone. The CPU plays a crucial role in maximising throughput, minimising bottlenecks, and ensuring seamless integration within a data centre.

In this article, we’ll explore why Intel Xeon processors are the preferred choice over AMD EPYC when paired with NVIDIA AI accelerators, focusing on architecture, software integration, and real-world performance benefits.

After speaking with multiple folks from NVIDIA at GTC, the reasoning was pretty clear:

1. PCIe and CXL Support – The Bandwidth Bottleneck

AI workloads rely heavily on the communication between CPUs and GPUs, and PCIe bandwidth plays a pivotal role in determining system performance. Both Intel Xeon (4th, 5th and 6th Gen) and AMD EPYC (Genoa, Bergamo, and Turin) support PCIe Gen 5, offering high-bandwidth connectivity for NVIDIA H100, H200, and B200 GPUs. However, Intel Xeon CPUs have key advantages:

• Lower latency PCIe root complexes: Intel’s PCIe implementation consistently exhibits lower latency than AMD’s, which improves data movement efficiency.
• Better support for CXL: With Compute Express Link (CXL) 1.1/2.0, Intel Xeon CPUs offer a more mature and optimised interconnect architecture, enabling faster access to shared memory pools, a growing requirement in AI inference workloads.

While AMD has CXL support, Intel has a deeper collaboration with enterprise partners to accelerate CXL adoption, ensuring superior memory pooling and coherent memory access with NVIDIA AI workloads.

2. NUMA Scaling and Multi-Socket Efficiency

AI inference and training workloads often involve high memory bandwidth requirements and benefit from well-optimised NUMA (Non-Uniform Memory Access) architectures.

• Intel Xeon’s UPI (Ultra Path Interconnect) enables high-bandwidth and low-latency communication between multiple sockets, which is crucial in multi-GPU systems.
• AMD’s Infinity Fabric can sometimes introduce bottlenecks, particularly when memory locality isn’t optimally managed in large-scale AI deployments.

For multi-socket configurations with multiple GPUs, Intel Xeon scales more efficiently due to better memory access patterns and inter-socket bandwidth tuning.

3. Optimised Software Stack for AI & HPC

Intel has long-established partnerships with NVIDIA, ensuring that its software stack is finely tuned for AI and HPC workloads. Key advantages include:

• OneAPI & AI acceleration: Intel’s OneAPI toolkit integrates well with NVIDIA libraries like cuDNN and TensorRT, ensuring optimised performance across heterogeneous workloads.
• Intel’s Deep Learning Boost (DL Boost): When handling pre-processing and mixed-precision AI inference workloads, AVX-512 and AMX instructions significantly accelerate tensor operations, reducing dependency on the GPU for every operation.
• Better support in NVIDIA CUDA and Triton Inference Server: Intel Xeon processors feature optimised threading models and memory handling within NVIDIA’s CUDA runtime, leading to more efficient execution of AI workloads.

AMD EPYC has improved in software support, but it still lacks the same level of deep integration and optimised AI acceleration that Intel Xeon enjoys with NVIDIA’s ecosystem.

4. Memory Bandwidth and AI Model Performance

AI models, particularly those running on NVIDIA H200 and B200 GPUs, demand high memory bandwidth to sustain inference and training workloads. While AMD EPYC offers higher memory bandwidth per socket, Intel Xeon provides:

• Tighter memory latency tuning, leading to more predictable performance across AI inference tasks.
• More optimised DDR5 and HBM2e handling, reducing contention in high-memory-bandwidth AI models.
• Better NUMA-aware scheduling, ensuring workloads are placed where memory access is fastest.

This means that AI models running across Intel Xeon + NVIDIA AI accelerators experience less variance in performance, particularly in multi-GPU and multi-node deployments.

5. Reliability, Security, and Enterprise Support

For large-scale AI deployments, reliability and security are paramount. Intel Xeon CPUs bring:

• Better security features like SGX (Software Guard Extensions) and TDX (Trusted Domain Extensions), ensuring AI workloads run securely in enterprise environments.
• Long-standing enterprise support from NVIDIA and cloud providers, ensuring seamless compatibility with NVIDIA DGX platforms and enterprise AI workloads.
• Mature firmware and BIOS support, reducing compatibility issues when deploying large-scale AI clusters.

While AMD EPYC has strong security features like SEV (Secure Encrypted Virtualization), Intel Xeon’s enterprise readiness and ecosystem stability make it the safer choice for mission-critical AI workloads.

Conclusion: Why Choose Intel Xeon for NVIDIA AI Accelerators?

When deploying NVIDIA H100, H200, or B200 AI accelerators, Intel Xeon CPUs provide lower latency, superior multi-socket scaling, better software optimisation, and higher reliability than AMD EPYC.

For enterprises and AI developers looking to maximise performance while maintaining stability, security, and scalability, Intel Xeon remains the best CPU choice for AI workloads running on NVIDIA GPUs.

Of course, it goes without saying that for AI workloads running on AMD MI Accelerators, AMD EPYC would be the logical choice since they are likely to be fine tuned for an AMD EPYC platform.