## NUMA-Aware GPU VMs: How NVIDIA's Reference Architecture Actually Fixes It

In [Part 1](https://docs.rafay.co/blog/2026/07/05/why-is-your-gpu-vm-slower-than-its-twin/) of the blog series, we walked through the basics: NUMA nodes, remote-access penalties, and why a GPU VM that doesn't know its own topology can quietly sabotage itself by pinning a hot thread to one vCPU while its data lands on a memory bank a socket away.

> We ended with a question we didn't answer: what does the fix actually look like in practice?

NVIDIA has published a prescriptive recommendation called **Performance Reference Architecture**. It's worth walking through this in some detail, because it's the closest thing to an industry-standard answer for **how do I make a GPU VM behave like bare metal?**.

## Why is your GPU VM slower Than Its Twin?

Picture two GPU VMs on the same bare-metal host, provisioned identically: same GPU model, same vCPU count, same memory allocation, same container image. One of them runs your inference workload **20% slower** than the other — every time, consistently, with no other tenant contention and nothing wrong in the logs.

The difference isn't the GPU. It's the **distance** between the CPU cores feeding that GPU and the memory those cores are reading from. That distance has a name **Non-Uniform Memory Access (NUMA)**. On modern multi-socket, multi-GPU hosts, it's one of the most common, least visible sources of inconsistent performance in GPU cloud environments.

At Rafay, we spend a lot of time thinking about how to make GPU infrastructure predictable because "predictable" is the whole point of a platform. So, we thought it is worth taking a step back and explaining what NUMA actually is, why it matters esp. once GPUs enter the picture, and what a platform needs to do about it.

This is the first part of a blog series on NUMA, how it impacts VMs esp. GPU VMs and approaches that are required to deliver great performance to GPU VMs on modern platforms.

## NVIDIA Performance Reference Architecture: An Introduction

Artificial intelligence (AI) and high-performance computing (HPC) workloads are evolving at unprecedented speed. Enterprises today require infrastructure that can scale elastically, provide consistent performance, and ensure secure multi-tenant operation. NVIDIA’s Performance Reference Architecture (PRA), built on HGX platforms with Shared NVSwitch GPU Passthrough Virtualization, delivers precisely this capability.

This is the introductory blog in a multi part series. In this blog, we explain **why PRA is critical** for modern enterprises and service providers, highlight the benefits of adoption, and outline the key steps required to successfully deploy and support the PRA design/architecture.