Multi-vGPU and P2P

Multi-vGPU and Peer-to-Peer (P2P) CUDA transfers are related capabilities for multi-GPU VM workloads. Use this hub for the shared hypervisor matrix, then open the concept or support page you need.

Table 47 Multi-vGPU and P2P pages

Page	Use when you need
Multi-vGPU	Concepts and compatibility rules for attaching several vGPUs to one VM
Multi-vGPU board support	Architecture-specific board and vGPU combinations for Multi-vGPU
Peer-to-Peer (P2P) CUDA Transfers	NVLink P2P between vGPUs in the same VM, profile tables, and the A100 / UVM caveat

Hypervisor Platform Support for Multi-vGPU and P2P

The hypervisor support below applies to both Multi-vGPU and P2P features.

Table 48 Hypervisor Platform Support for Multi-vGPU and P2P

Hypervisor Platform	NVIDIA AI Enterprise Infra Release	Supported vGPU Types	Documentation
Red Hat Enterprise Linux with KVM	All active NVIDIA AI Enterprise Infra Releases	All NVIDIA vGPU for Compute with PCIe GPUs; on supported GPUs, both time-sliced and MIG-backed vGPUs are supported.	Setting up Multi-vGPU VMs on RHEL KVM
Ubuntu with KVM	All active NVIDIA AI Enterprise Infra Releases	All NVIDIA vGPU for Compute with PCIe GPUs; on supported GPUs, both time-sliced and MIG-backed vGPUs are supported.	Setting up Multi-vGPU VMs on Ubuntu KVM
VMware vSphere	All active NVIDIA AI Enterprise Infra Releases	Time-sliced multi-vGPU on supported GPUs. MIG-backed multi-vGPU requires VMware Cloud Foundation (VCF) 9.1 or later (NVIDIA AI Enterprise Infra 8.2 / vGPU 20.2 and later).	Setting up Multi-vGPU on VMware vSphere 8

Note

P2P CUDA transfers are not supported on Windows. Only Linux OS distros as outlined in NVIDIA AI Enterprise Infrastructure Support Matrix are supported.

Note

MIG-backed multi-vGPU on VMware vSphere requires VMware VCF 9.1 or later. It is not supported on VCF 9.0P01 or on standalone ESXi without VCF 9.1. Linux KVM hypervisors continue to support MIG-backed multi-vGPU on supported GPUs.