NVIDIA AI Enterprise Release Notes

Release information for all users of NVIDIA AI Enterprise.

1. What's New in NVIDIA AI Enterprise

Changes in this release of NVIDIA AI Enterprise are as follows:

  • New releases of the following software components of NVIDIA AI Enterprise
    • GPU Operator: 1.11
    • Network Operator: 1.2
    • NVIDIA deep learning frameworks: 22.05-nvaie2.1
    • NVIDIA vGPU software: 14.1

      This release includes security updates for the NVIDIA Virtual GPU Manager and graphics driver for Linux- see Security Bulletin: NVIDIA GPU Display Driver - May 2022, which is listed on the NVIDIA Product Security page

    • NVIDIA RAPIDS: 22.04-cuda11.4-ubuntu20.04-py3.8
    • TAO Toolkit: 22.05
  • Support for the following OS releases as a guest OS and in bare-metal deployments:
    • Ubuntu 22.04 LTS
    • Red Hat Enterprise Linux 9.0
  • Support for GPU Operator and Network Operator with Ubuntu 22.04 LTS
  • Support for GPU Operator with RedHat OpenShift on multicloud instances that support NVIDIA AI Enterprise
  • Miscellaneous bug fixes

2. Supported Hardware and Software

For more information, refer to the NVIDIA AI Enterprise Product Support Matrix.

NVIDIA GPUs

  • NVIDIA A100X
  • NVIDIA A100 PCIe 40GB
  • NVIDIA A100 HGX 40GB
  • NVIDIA A100 PCIe 80GB
  • NVIDIA A100 HGX 80GB
  • NVIDIA A40
  • NVIDIA A30X
  • NVIDIA A30
  • NVIDIA A10
  • NVIDIA A16
  • NVIDIA A2
  • NVIDIA RTX A6000
  • NVIDIA RTX A5500
  • NVIDIA RTX A5000
  • NVIDIA T4
  • NVIDIA V100

For NVIDIA AI Enterprise Compatible servers, refer to the NVIDIA-certified systems page.

Multi-node scaling requires an Ethernet NIC that supports RoCE. For best performance, NVIDIA recommends using an NVIDIA® Mellanox® ConnectX®-6 Dx and an NVIDIA A100 GPU in each VM used for multi-node scaling. Refer to the Sizing guide and the Multi-Node Training solution guide for further information.

Hypervisor Software Supported

  • VMware vSphere Hypervisor (ESXi) Enterprise Plus Edition 7.0 Update 3
  • VMware vCenter Server 7.0 Update 3
  • VMware vSphere 6.7 (only for NVIDIA T4 and NVIDIA V100 GPUs)

Guest Operating Systems Supported

  • Ubuntu 22.04 LTS
  • Ubuntu 20.04 LTS
  • Red Hat Enterprise Linux 9.0
  • Red Hat Enterprise Linux 8.4
  • Red Hat OpenShift 4.9 and later
Note: Red Hat Enterprise Linux guest OS support is limited to running containers by using Docker without Kubernetes. NVIDIA AI Enterprise features that depend on Kubernetes, for example, the use of GPU Operator, are not supported on Red Hat Enterprise Linux.

2.1. NVIDIA AI Enterprise Software Components

Software Component NVIDIA Release
NVIDIA vGPU Software 14.1:
  • Virtual GPU Manger: 510.73.06
  • Graphics Driver for Linux: 510.73.08
NVIDIA GPU Operator 1.11
NVIDIA Network Operator 1.2
TensorFlow 2 22.02-tf2-nvaie-2.0-py3
TensorFlow 1 22.02-tf1-nvaie-2.0-py3
PyTorch 22.02-nvaie-2.0-py3
NVIDIA Triton Inference Server 22.02-nvaie-2.0-py3 and 22.02-nvaie-2.0-py3-sdk
NVIDIA TensorRT 22.02-nvaie-2.0-py3
NVIDIA RAPIDS 22.04-cuda11.4-ubuntu20.04-py3.8
TAO Toolkit for Language Model (Conv AI) 3.22.05-py3
TAO Toolkit for Conv AI 3.22.05-py3
TAO Toolkit for CV 3.22.05-tf1.15.4-py3-nvaie

2.2. Switching the Mode of a GPU that Supports Multiple Display Modes

Some GPUs support displayless and display-enabled modes but must be used in NVIDIA AI Enterprise deployments in displayless mode.

The GPUs listed in the following table support multiple display modes. As shown in the table, some GPUs are supplied from the factory in displayless mode, but other GPUs are supplied in a display-enabled mode.

GPU Mode as Supplied from the Factory
NVIDIA A40 Displayless
NVIDIA RTX A5000 Display enabled
NVIDIA RTX A5500 Display enabled
NVIDIA RTX A6000 Display enabled

A GPU that is supplied from the factory in displayless mode, such as the NVIDIA A40 GPU, might be in a display-enabled mode if its mode has previously been changed.

To change the mode of a GPU that supports multiple display modes, use the displaymodeselector tool, which you can request from the NVIDIA Display Mode Selector Tool page on the NVIDIA Developer website.

Note:

Only the following GPUs support the displaymodeselector tool:

  • NVIDIA A40
  • NVIDIA RTX A5000
  • NVIDIA RTX A5500
  • NVIDIA RTX A6000

Other GPUs that support NVIDIA AI Enterprise do not support the displaymodeselector tool and, unless otherwise stated, do not require display mode switching.

2.3. Requirements for Using C-Series vCS vGPUs

Because C-Series vCS vGPUs have large BAR memory settings, using these vGPUs has some restrictions on VMware ESXi.

2.4. Requirements for Using vGPU on GPUs Requiring 64 GB or More of MMIO Space with Large-Memory VMs

Some GPUs require 64 GB or more of MMIO space. When a vGPU on a GPU that requires 64 GB or more of MMIO space is assigned to a VM with 32 GB or more of memory on ESXi , the VM’s MMIO space must be increased to the amount of MMIO space that the GPU requires.

For more information, refer to VMware Knowledge Base Article: VMware vSphere VMDirectPath I/O: Requirements for Platforms and Devices (2142307).

No extra configuration is needed.

The following table lists the GPUs that require 64 GB or more of MMIO space and the amount of MMIO space that each GPU requires.

GPU MMIO Space Required
NVIDIA A10 64 GB
NVIDIA A30 64 GB
NVIDIA A40 128 GB
NVIDIA A100 40GB (all variants) 128 GB
NVIDIA A100 80GB (all variants) 256 GB
NVIDIA RTX A5000 64 GB
NVIDIA RTX A5500 64 GB
NVIDIA RTX A6000 128 GB
Tesla P100 (all variants) 64 GB

2.5. Linux Only: Error Messages for Misconfigured GPUs Requiring Large MMIO Space

In a Linux VM, if the requirements for using C-Series vCS vGPUs or GPUs requiring large MMIO space in pass-through mode are not met, the following error messages are written to the VM's dmesg log during installation of the NVIDIA AI Enterprise graphics driver:

NVRM: BAR1 is 0M @ 0x0 (PCI:0000:02:02.0)
[  90.823015] NVRM: The system BIOS may have misconfigured your GPU.
[  90.823019] nvidia: probe of 0000:02:02.0 failed with error -1
[  90.823031] NVRM: The NVIDIA probe routine failed for 1 device(s).

2.6. NVIDIA CUDA Toolkit Version Support

The releases in this release family of NVIDIA AI Enterprise support NVIDIA CUDA Toolkit 11.4.

For more information about NVIDIA CUDA Toolkit, see CUDA Toolkit 11.4 Documentation.

2.7. vGPU Migration Support

vGPU migration, which includes vMotion and suspend-resume, is supported for both time-sliced and MIG-backed vGPUs on all supported GPUs, hypervisor software releases, and guest operating systems.

Note: vGPU migration is disabled for a VM for which any of the following NVIDIA CUDA Toolkit features is enabled:
  • Unified memory
  • Debuggers
  • Profilers

Known Issues with vGPU Migration Support

Use Case Affected GPUs Issue
Migration between hosts with different ECC memory configuration All GPUs that support vGPU migration Migration of VMs configured with vGPU stops before the migration is complete

2.8. Multiple vGPU Support

To support applications and workloads that are compute or graphics intensive, multiple vGPUs can be added to a single VM. The assignment of more than one vGPU to a VM is supported only on a subset of vGPUs and VMware vSphere Hypervisor (ESXi) releases.

Supported vGPUs

Only Q-series and C-series time-sliced vGPUs that are allocated all of the physical GPU's frame buffer are supported. MIG-backed vGPUs are not supported.

GPU Architecture Board vGPU
Ampere (compute workloads only) NVIDIA A100 PCIe 80GB

NVIDIA A100X

A100D-80C See Note (1).
NVIDIA A100 HGX 80GB A100DX-80C See Note (1).
NVIDIA A100 PCIe 40GB A100-40C See Note (1).
NVIDIA A100 HGX 40GB A100X-40C See Note (1).
NVIDIA A30

NVIDIA A30X

A30-24C See Note (1).
Ampere (compute and graphics workloads) NVIDIA A40 A40-48Q See Note (1).
A40-48C See Note (1).
NVIDIA A16 A16-16Q See Note (1).
A16-16C See Note (1).
NVIDIA A10 A10-24Q See Note (1).
A10-24C See Note (1).
NVIDIA A2 A2-16Q See Note (1).
A2-16C See Note (1).
NVIDIA RTX A6000 A6000-48Q See Note (1).
A6000-48C See Note (1).
NVIDIA RTX A5500 A5500-24Q See Note (1).
A5500-24C See Note (1).
NVIDIA RTX A5000 A5000-24Q See Note (1).
A5000-24C See Note (1).
Turing Tesla T4 T4-16Q
T4-16C
Volta Tesla V100 SXM2 32GB V100DX-32Q
V100D-32C
Tesla V100 PCIe 32GB V100D-32Q
V100D-32C
Tesla V100S PCIe 32GB V100S-32Q
V100S-32C
Tesla V100 SXM2 V100X-16Q
V100X-16C
Tesla V100 PCIe V100-16Q
V100-16C
Tesla V100 FHHL V100L-16Q
V100L-16C
Note:
  1. This type of vGPU cannot be assigned with other types of vGPU to the same VM.

Maximum vGPUs per VM

NVIDIA AI Enterprise supports up to a maximum of four vGPUs per VM on VMware vSphere Hypervisor (ESXi).

Supported Hypervisor Releases

All hypervisors that support NVIDIA AI Enterprise are supported.

2.9. Peer-to-Peer CUDA Transfers over NVLink Support

Peer-to-peer CUDA transfers enable device memory between vGPUs on different GPUs that are assigned to the same VM to be accessed from within the CUDA kernels. NVLink is a high-bandwidth interconnect that enables fast communication between such vGPUs. Peer-to-Peer CUDA transfers over NVLink are supported only on a subset of vGPUs, VMware vSphere Hypervisor (ESXi) releases, and guest OS releases.

Supported vGPUs

Only Q-series and C-series time-sliced vGPUs that are allocated all of the physical GPU's frame buffer on physical GPUs that support NVLink are supported.

GPU Architecture Board vGPU
Ampere (compute workloads only) NVIDIA A100 PCIe 80GB

NVIDIA A100X

A100D-80C
NVIDIA A100 HGX 80GB A100DX-80C See Note (1).
NVIDIA A100 PCIe 40GB A100-40C
NVIDIA A100 HGX 40GB A100X-40C See Note (1).
NVIDIA A30

NVIDIA A30X

A30-24C
Ampere (compute and graphics workloads) NVIDIA A40 A40-48Q
A40-48C
NVIDIA A10 A10-24Q
A10-24C
NVIDIA RTX A6000 A6000-48Q
A6000-48C
NVIDIA RTX A5500 A5500-24Q
A5500-24C
NVIDIA RTX A5000 A5000-24Q
A5000-24C
Volta Tesla V100 SXM2 32GB V100DX-32Q
V100DX-32C
Tesla V100 SXM2 V100X-16Q
V100X-16C
NVIDIA RTX A5000 A5500-24Q
Note:
  1. Supported only on the following hardware:
    • NVIDIA HGX™ A100 4-GPU baseboard with four fully connected GPUs

Supported Hypervisor Releases

Peer-to-Peer CUDA Transfers over NVLink are supported on all hypervisor releases that support the assignment of more than one vGPU to a VM. For details, see Multiple vGPU Support.

Supported Guest OS Releases

Linux only. Peer-to-Peer CUDA Transfers over NVLink are not supported on Windows.

Limitations

  • Only direct connections are supported. NVSwitch is not supported.
  • Only time-sliced vGPUs are supported. MIG-backed vGPUs are not supported.
  • PCIe is not supported.
  • SLI is not supported.

2.10. Unified Memory Support

Unified memory is a single memory address space that is accessible from any CPU or GPU in a system. It creates a pool of managed memory that is shared between the CPU and GPU to provide a simple way to allocate and access data that can be used by code running on any CPU or GPU in the system. Unified memory is supported only on a subset of vGPUs and guest OS releases.

Note:Unified memory is disabled by default. If used, you must enable unified memory individually for each vGPU that requires it by setting a vGPU plugin parameter. NVIDIA CUDA Toolkit profilers are supported and can be enabled on a VM for which unified memory is enabled.

Supported vGPUs

Only time-sliced vGPUs that are allocated all of the physical GPU's frame buffer on physical GPUs that support unified memory are supported.

GPU Architecture Board vGPU
Ampere NVIDIA A40 A40-48C
NVIDIA A16 A16-16C
NVIDIA A10 A10-24C
NVIDIA A2 A2-16C
NVIDIA RTX A6000 A6000-48C
NVIDIA RTX A5500 A5500-24C
NVIDIA RTX A5000 A5000-24C

Supported Guest OS Releases

Linux only. Unified memory is not supported on Windows.

Limitations

  • When unified memory is enabled for a VM, vGPU migration is disabled for the VM.

2.11. NVIDIA GPU Operator Support

NVIDIA GPU Operator simplifies the deployment of NVIDIA AI Enterprise with software container platforms. NVIDIA GPU Operator is supported only on specific combinations of VMware vSphere Hypervisor (ESXi) release, container platform, and guest OS release.

VMware vSphere Hypervisor (ESXi) Release Container Platform Guest OS
VMware vSphere Hypervisor (ESXi) 7.0 Update 2 Kubernetes 1.21 or later compatible versions Ubuntu 22.04 LTS
VMware vSphere Hypervisor (ESXi) 7.0 Update 2 Kubernetes 1.21 or later compatible versions Ubuntu 20.04 LTS

3. NVIDIA AI Enterprise Supported Cloud Services

NVIDIA AI Enterprise is supported on several cloud services with bring-your-own-license (BYOL) licensing.

3.1. Amazon Web Services Elastic Compute Cloud (AWS EC2)

GPU Supported AWS EC2 Instances Supported Guest Operating Systems
NVIDIA T4

g4dn.xlarge

g4dn.2xlarge

g4dn.4xlarge

g4dn.8xlarge

g4dn.12xlarge

g4dn.16xlarge

Red Hat Enterprise Linux 8.4

Ubuntu 22.04

Ubuntu 20.04

NVIDIA V100

P3.2xlarge

P3.8xlarge

P3.16xlarge

NVIDIA A10G

g5.xlarge

g5.2xlarge

g5.4xlarge

g5.8xlarge

g5.12xlarge

g5.16xlarge

g5.24xlarge

g5.48xlarge

NVIDIA A100

p4d.24xlarge

3.2. Google Cloud Platform (GCP)

GPU Supported GCP Instances Supported Guest Operating Systems
Tesla T4

Any predefined machine type.

Any custom machine type that can be created in a zone.

Red Hat Enterprise Linux 8.4

Ubuntu 22.04

Ubuntu 20.04

Tesla V100
Tesla A100

a2-highgpu-1g

a2-highgpu-2g

a2-highgpu-4g

a2-highgpu-8g

a2-megagpu-16g

3.3. Microsoft Azure

GPU Supported Azure Instances Supported Guest Operating Systems
NVIDIA V100

NC6s_v3

NC12s_v3

NC24s_v3

NC24rs_v3

ND40rs_v2

Red Hat Enterprise Linux 8.4

Ubuntu 22.04

Ubuntu 20.04

NVIDIA T4

NC4asT4_v3

NC8asT4_v3

NC16asT4_v3

NC64asT4_v3

NVIDIA A100

ND96asr_v4

ND96amsr_A100_v4

NVIDIA A10

NV6ads_A10_v5

NV12ads_A10_v5

NV18ads_A10_v5

NV36ads_A10_v5

NV36adms_A10_v5

NV72ads_A10_v5

3.4. NVIDIA GPU Optimized VMI on CSP Marketplace

For ease of use in the cloud, NVIDIA will also provide compute optimized and validated base Virtual Machine Instances (VMI) through CSP marketplaces. The VMI includes key technologies and software from NVIDIA for rapid deployment, management, and scaling of AI workloads in the modern hybrid cloud.

The VMI will have the following software pre-installed:

  • Ubuntu Server 20.04

  • NVIDIA driver 470TRD - 470.103.01

  • Docker-ce 20.10.12

  • NVIDIA Container Toolkit 1.8.1

  • NVIDIA Container Runtime 3.8.1

4. CPU Only Server Support

NVIDIA AI Enterprise supports deployments on CPU only servers that are part of the NVIDIA Certfied Systems list. Customers can deploy both GPU and CPU Only systems with VMware vSphere or Red Hat Enterprise Linux.

NVIDIA AI Enterprise will support the following CPU enabled frameworks:

  • TensorFlow

  • PyTorch

  • Triton Inference Server with FIL backend

  • NVIDIA RAPIDS with XGBoost and Dask

5. Known Product Limitations

Known product limitations for this release of NVIDIA AI Enterprise are described in the following sections.

5.1. Issues occur when the channels allocated to a vGPU are exhausted

Description

Issues occur when the channels allocated to a vGPU are exhausted and the guest VM to which the vGPU is assigned fails to allocate a channel to the vGPU. A physical GPU has a fixed number of channels and the number of channels allocated to each vGPU is inversely proportional to the maximum number of vGPUs allowed on the physical GPU.

When the channels allocated to a vGPU are exhausted and the guest VM fails to allocate a channel, the following errors are reported on the hypervisor host or in an NVIDIA bug report:

Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): Guest attempted to allocate channel above its max channel limit 0xfb
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): VGPU message 6 failed, result code: 0x1a
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0):         0xc1d004a1, 0xff0e0000, 0xff0400fb, 0xc36f,
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0):         0x1, 0xff1fe314, 0xff1fe038, 0x100b6f000, 0x1000,
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0):         0x80000000, 0xff0e0200, 0x0, 0x0, (Not logged),
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0):         0x1, 0x0
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): , 0x0

Workaround

Use a vGPU type with more frame buffer, thereby reducing the maximum number of vGPUs allowed on the physical GPU. As a result, the number of channels allocated to each vGPU is increased.

5.2. Total frame buffer for vGPUs is less than the total frame buffer on the physical GPU

Some of the physical GPU's frame buffer is used by the hypervisor on behalf of the VM for allocations that the guest OS would otherwise have made in its own frame buffer. The frame buffer used by the hypervisor is not available for vGPUs on the physical GPU. In NVIDIA vGPU deployments, frame buffer for the guest OS is reserved in advance, whereas in bare-metal deployments, frame buffer for the guest OS is reserved on the basis of the runtime needs of applications.

If error-correcting code (ECC) memory is enabled on a physical GPU that does not have HBM2 memory, the amount of frame buffer that is usable by vGPUs is further reduced. All types of vGPU are affected, not just vGPUs that support ECC memory.

On all GPUs that support ECC memory and, therefore, dynamic page retirement, additional frame buffer is allocated for dynamic page retirement. The amount that is allocated is inversely proportional to the maximum number of vGPUs per physical GPU. All GPUs that support ECC memory are affected, even GPUs that have HBM2 memory or for which ECC memory is disabled.

The approximate amount of frame buffer that NVIDIA AI Enterprise reserves can be calculated from the following formula:

max-reserved-fb = vgpu-profile-size-in-mb÷16 + 16 + ecc-adjustments + page-retirement-allocation + compression-adjustment

max-reserved-fb
The maximum total amount of reserved frame buffer in Mbytes that is not available for vGPUs.
vgpu-profile-size-in-mb
The amount of frame buffer in Mbytes allocated to a single vGPU. This amount depends on the vGPU type. For example, for the T4-16Q vGPU type, vgpu-profile-size-in-mb is 16384.
ecc-adjustments
The amount of frame buffer in Mbytes that is not usable by vGPUs when ECC is enabled on a physical GPU that does not have HBM2 memory.
  • If ECC is enabled on a physical GPU that does not have HBM2 memory ecc-adjustments is fb-without-ecc/16, which is equivalent to 64 Mbytes for every Gbyte of frame buffer assigned to the vGPU. fb-without-ecc is total amount of frame buffer with ECC disabled.
  • If ECC is disabled or the GPU has HBM2 memory, ecc-adjustments is 0.
page-retirement-allocation
The amount of frame buffer in Mbytes that is reserved for dynamic page retirement.
  • On GPUs based on the NVIDIA Maxwell GPU architecture, page-retirement-allocation = 4÷max-vgpus-per-gpu.
  • On GPUs based on NVIDIA GPU architectures after the Maxwell architecture, page-retirement-allocation = 128÷max-vgpus-per-gpu
max-vgpus-per-gpu
The maximum number of vGPUs that can be created simultaneously on a physical GPU. This number varies according to the vGPU type. For example, for the T4-16Q vGPU type, max-vgpus-per-gpu is 1.
compression-adjustment

The amount of frame buffer in Mbytes that is reserved for the higher compression overhead in vGPU types with 12 Gbytes or more of frame buffer on GPUs based on the Turing architecture.

compression-adjustment depends on the vGPU type as shown in the following table.

vGPU Type Compression Adjustment (MB)

T4-16Q

T4-16C

T4-16A

28

For all other vGPU types, compression-adjustment is 0.

5.3. Single vGPU benchmark scores are lower than pass-through GPU

Description

A single vGPU configured on a physical GPU produces lower benchmark scores than the physical GPU run in pass-through mode.

Aside from performance differences that may be attributed to a vGPU’s smaller frame buffer size, vGPU incorporates a performance balancing feature known as Frame Rate Limiter (FRL). On vGPUs that use the best-effort scheduler, FRL is enabled. On vGPUs that use the fixed share or equal share scheduler, FRL is disabled.

FRL is used to ensure balanced performance across multiple vGPUs that are resident on the same physical GPU. The FRL setting is designed to give good interactive remote graphics experience but may reduce scores in benchmarks that depend on measuring frame rendering rates, as compared to the same benchmarks running on a pass-through GPU.

Resolution

FRL is controlled by an internal vGPU setting. On vGPUs that use the best-effort scheduler, NVIDIA does not validate vGPU with FRL disabled, but for validation of benchmark performance, FRL can be temporarily disabled by adding the configuration parameter pciPassthru0.cfg.frame_rate_limiter in the VM’s advanced configuration options.

Note: This setting can only be changed when the VM is powered off.
  1. Select Edit Settings.
  2. In Edit Settings window, select the VM Options tab.
  3. From the Advanced drop-down list, select Edit Configuration.
  4. In the Configuration Parameters dialog box, click Add Row.
  5. In the Name field, type the parameter name pciPassthru0.cfg.frame_rate_limiter, in the Value field type 0, and click OK.



    Screen capture showing a dialog box that contains advanced VM configuration options

With this setting in place, the VM’s vGPU will run without any frame rate limit. The FRL can be reverted back to its default setting by setting pciPassthru0.cfg.frame_rate_limiter to 1 or by removing the parameter from the advanced settings.

5.4. VMs configured with large memory fail to initialize vGPU when booted

Description

When starting multiple VMs configured with large amounts of RAM (typically more than 32GB per VM), a VM may fail to initialize vGPU. In this scenario, the VM boots in VMware SVGA mode and doesn’t load the NVIDIA driver. The NVIDIA AI Enterprise GPU is present in Windows Device Manager but displays a warning sign, and the following device status:

Windows has stopped this device because it has reported problems. (Code 43)

The VMware vSphere VM’s log file contains these error messages:

vthread10|E105: NVOS status 0x29
vthread10|E105: Assertion Failed at 0x7620fd4b:179
vthread10|E105: 8 frames returned by backtrace
 ...
vthread10|E105: VGPU message 12 failed, result code: 0x29
...
vthread10|E105: NVOS status 0x8
vthread10|E105: Assertion Failed at 0x7620c8df:280
vthread10|E105: 8 frames returned by backtrace
...
vthread10|E105: VGPU message 26 failed, result code: 0x8

Resolution

vGPU reserves a portion of the VM’s framebuffer for use in GPU mapping of VM system memory. The reservation is sufficient to support up to 32GB of system memory, and may be increased to accommodate up to 64GB by adding the configuration parameter pciPassthru0.cfg.enable_large_sys_mem in the VM’s advanced configuration options

Note: This setting can only be changed when the VM is powered off.
  1. Select Edit Settings.
  2. In Edit Settings window, select the VM Options tab.
  3. From the Advanced drop-down list, select Edit Configuration.
  4. In the Configuration Parameters dialog box, click Add Row.
  5. In the Name field, type the parameter name pciPassthru0.cfg.enable_large_sys_mem, in the Value field type 1, and click OK.

With this setting in place, less GPU framebuffer is available to applications running in the VM. To accommodate system memory larger than 64GB, the reservation can be further increased by adding pciPassthru0.cfg.extra_fb_reservation in the VM’s advanced configuration options, and setting its value to the desired reservation size in megabytes. The default value of 64M is sufficient to support 64 GB of RAM. We recommend adding 2 M of reservation for each additional 1 GB of system memory. For example, to support 96 GB of RAM, set pciPassthru0.cfg.extra_fb_reservation to 128.

The reservation can be reverted back to its default setting by setting pciPassthru0.cfg.enable_large_sys_mem to 0, or by removing the parameter from the advanced settings.

6. Known Issues

6.1. The NVIDIA MOFED driver container fails to install the driver if Network Operator is installed

Description

The NVIDIA MOFED driver container fails to install the driver if Network Operator is installed. The installation fails because the container fails to unload the ib_core module. The rdma-core package is installed as part of the Red Hat CoreOS installation. This package loads the ib_core module if the system has Mellanox network interface cards (NICs).

Status

Open

Ref. #

3565857

6.2. Migration of VMs configured with vGPU stops before the migration is complete

Description

When a VM configured with vGPU is migrated to another host, the migration stops before it is complete. After the migration stops, the VM is no longer accessible.

This issue occurs if the ECC memory configuration (enabled or disabled) on the source and destination hosts are different. The ECC memory configuration on both the source and destination hosts must be identical.

Workaround

Reboot the hypervisor host to recover the VM. Before attempting to migrate the VM again, ensure that the ECC memory configuration on both the source and destination hosts are identical.

Status

Not an NVIDIA bug

A fix is that prevents the VM from becoming inaccessible is available from VMware in VMware vSphere Hypervisor (ESXi) 6.7 Update 3 patch 16075168-04282020. Even with this patch, migration of a VM configured with vGPU requires the ECC memory configuration on both the source and destination hosts to be identical.

Ref. #

200520027

Notices

Notice

This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.

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