Version 535.216.03 (Linux)

Release notes for the Release 535 family of NVIDIA® Data Center GPU Drivers for Linux and Windows.

This edition of Release Notes describes the Release 535 family of NVIDIA® Data Center GPU Drivers for Linux and Windows. NVIDIA provides these notes to describe performance improvements, bug fixes and limitations in each documented version of the driver.

1. Version Highlights

This section provides highlights of the NVIDIA Data Center GPU R535 Driver (version 535.216.03 Linux.

For changes related to the 535 release of the NVIDIA display driver, review the file "NVIDIA_Changelog" available in the .run installer packages.

  • Linux driver release date: 11/19/2024

1.1. Software Versions

For this release, the software versions are as follows:

  • CUDA Toolkit 12: 12.2.2

    Note that starting with CUDA 11, individual components of the toolkit are versioned independently. For a full list of the individual versioned components (for example, nvcc, CUDA libraries, and so on), see the CUDA Toolkit Release Notes.

  • NVIDIA Data Center GPU Driver: 535.216.03 (Linux)

  • Fabric Manager: 535.216.03 (Use nv-fabricmanager -v)

  • NVFlash: 5.791

For more information on getting started with the NVIDIA Fabric Manager on NVSwitch-based systems (for example, NVIDIA HGX A100), refer to the Fabric Manager User Guide.

1.2. Fixed Issues

  • Increased the address space size for GSP Firmware tasks to support higher FB memory boards. [4904263]
  • Unnecessary register writes from the kernel driver were removed, so an IO error is no longer triggered when upgrading to FW 1.6 with new security updates. [4899198]

1.3. Known Issues

General

  • UVM may fail to initialize correctly when both MIG and HMM are used on systems with a large number of GPUs containing large amounts of GPU memory. To workaround this issue HMM may be disabled by passing uvm_disable_hmm=1 when loading the nvidia-uvm.ko kernel module. Doing so will prevent usage of HMM features such as system allocated memory. Alternatively MIG may be disabled. 4435278
  • "Change ECC State" and "Enable Error Correction Code" do not change synchronously when ECC state changes. 3838953
  • CUDA kernels that use the sparsity feature of tensor cores through the mma.sp PTX instruction on Hopper architecture GPUs may intermittently experience silent data corruption resulting in incorrect results. NVIDIA libraries currently do not provide access to tensor cores with sparsity so only kernels directly developed using the mma.sp PTX instruction are impacted. This issue will be fixed in an upcoming release. 4254491
  • cpufreq-info is not reporting correct core frequency on Grace. 4253549

    The CPU frequency reported by the Linux kernel might vary significantly from the actual value.

    WorkaroundThere is currently no workaround, and this issue will be resolved in a future kernel version.

  • Grace is doing a seemingly unnecessary munmap of the untouched host memory when CUDA context is active. 4052424

    Customers who use older stable/longterm trees should update to the latest upstream subversion, and customers who are using another tree should cherry-pick the subversion from one of the trees (versions linux-6.5.y, linux-6.1.y, or linux-5.15.y).

    Workaround

    There is currently no workaround.

  • Support for 4k page size in nv-p2p. – 4316020

    The NVIDIA driver's kernel mode GPUDirect RDMA APIs that are used for Peer-direct support in MLNX_OFED and GPUDirect Storage are not supported on GH200 platforms when used with Linux kernels that are configured with the 4K page size. These APIs are not functional and might lead to a kernel memory corruption.

    Users are strongly encouraged to move their software stack to the dma-buf APIs, which requires the open-source GPU driver, Linux kernel 5.12 or later, and NVIDIA Turing™ + GPU. Since the dma-buf APIs work correctly on 4K page kernels, using the APIs is ideal mitigation for this issue.
  • Graphics functionalities, such as EGL, GLX, and Vulcan, are currently not supported under the 4K OS page size – 4333780

    Workaround

    We recommend that you use a 64K page size.

  • The SLES driver RPM package might have issues with 64k kernels. – 4251209

    WorkaroundUse the 64k kernel support packages from the distro package maintainer (https://en.opensuse.org/SDB:NVIDIA_drivers) or use the .run file install method.

  • Set_declare SMBPBI Master caps will return success. – 3977576

    The undocumented SMBPBI opcode 0x8 does not return ERR_OPCODE in conformance with the SMBPBI specification. This opcode is not supported on the Hopper HGX 8-GPU baseboard and should not be used.

  • All Mellanox ports are shown by command "nvidia-smi topo -m" after dual-port NICs are bonded.
  • When polling the H100 GPU via SMBPBI using GPU Performance Monitoring metrics, driver reloads or GPU resets can result in driver errors that manifest as PID (X62) errors on Linux. NVIDIA is investigating this issue.
  • On NVIDIA H800, monitoring software such as DCGM or NVML might report lower double-precision (FP64) utilization metrics. This is expected as per the NVIDIA H800 product configuration. Refer to the NVIDIA H800 product brief for more details.
  • The GPU driver build system might not pick the Module.symvers file, produced when building the ofa_kernel module from MLNX_OFED, from the right subdirectory. Because of that, nvidia_peermem.ko does not have the right kernel symbol versions for the APIs exported by the IB core driver, and therefore it does not load correctly. That happens when using MLNX_OFED 5.5 or newer on a Linux Arm64 or ppc64le platform.
    To work around this issue, perform the following:
    1. Verify that nvidia_peermem.ko does not load correctly.
    2. Uninstall old MLNX_OFED if one was installed.
    3. Manually remove /usr/src/ofa_kernel/default if one exists.
    4. Install MLNX_OFED 5.5 or newer.
    5. Manually create a soft link:
       /usr/src/ofa_kernel/default -> /usr/src/ofa_kernel/$(uname -m)/$(uname -r)
    6. Reinstall the GPU driver.
  • If you encounter an error on RHEL7 when installing with cuda-drivers-fabricmanager packages, use the following alternate instructions. For example:

    If you are upgrading from a different branch, for example to driver 515.65.01:

    new_version=515.65.01
    sudo yum swap nvidia-driver-latest-dkms nvidia-driver-latest-dkms-${new_version}
    sudo yum install nvidia-fabric-manager-${new_version}
  • When installing a driver on SLES15 or openSUSE15 that previously had an R515 driver installed, users need to run the following command afterwards to finalize the installation:

    sudo zypper install --force nvidia-gfxG05-kmp-default 

    Without doing this, users may see the kernel objects as missing.

  • nvidia-release-upgrade
    may report that not all updates have been installed and exit.
    When running the
    nvidia-release-upgrade
    command on DGX systems running DGX OS 4.99.x, it may exit and tell users: "Please install all available updates for your release before upgrading" even though all upgrades have been installed.
    Users who see this can run the following command:
    sudo apt install -y nvidia-fabricmanager-450/bionic-updates --allow-downgrades
    After running this, proceed with the regular upgrade steps:
    sudo apt update
    sudo apt full-upgrade -y 
    sudo apt install -y nvidia-release-upgrade 
    sudo nvidia-release-upgrade
  • By default, Fabric Manager runs as a systemd service. If using
    DAEMONIZE=0
    in the Fabric Manager configuration file, then the following steps may be required.
    1. Disable FM service from auto starting.
      systemctl disable nvidia-fabricmanager
    2. Once the system is booted, manually start FM process.
      /usr/bin/nv-fabricmanager -c /usr/share/nvidia/nvswitch/fabricmanager.cfg
      Note, since the process is not a daemon, the SSH/Shell prompt will not be returned (use another SSH shell for other activities or run FM as a background task).
  • Important correctness fix for H100 GPU instructions used by cuBLAS, other CUDA libraries, and user CUDA code

    An issue was discovered recently with H100 GPUs (H100 PCIe and HGX H100) where certain operations put the GPU in an invalid state that allowed some GPU instructions to operate at unsupported frequency that can result in incorrect computation results and faster than expected performance. The affected GPU instructions are used by cuBLAS, other CUDA libraries, and can also be used for user CUDA code.

    The operations that allow the GPU to enter an invalid state are the following:
    • Enabling MIG
    • Deinitialize and reinitialize the GPU (for example, turn off persistence mode and turn it back on or reload the nvidia.ko driver)
    • Any Compute Engine error (for example, MMU fault, Out of Range warp error, and so on)
    Once the GPU enters the invalid state, the performance for some GPU instructions is increased by 7-10%, but the computation results may be incorrect.

    The current release fixes this issue, and it is no longer possible to enter the invalid GPU state. This issue has been present in all drivers since the H100 launch, and we recommend that you upgrade to the current release as soon as possible. If upgrading is not immediately possible, a GPU reset can restore the GPU back to the correct operational state, except for when MIG is being used. For MIG, the new driver is required, and there is no workaround available.

  • Uninstalling the driver fails, and the system reboots automatically.
    On Windows 2019 and 2022 servers, uninstalling the driver causes the system to restart automatically before the uninstallation is completed. The issue also occurs when you upgrade the driver from an older version to a new version, even after selecting the Perform Clean Installation option in the installer UI.
    Note: This issue does not occur in Linux.

    Workaround

    We strongly recommend that you always install, uninstall, and upgrade drivers from Safe mode.

  • In Shared Switch virtualization mode, the guest VM GPU driver load and unload stress test fails after certain iteration

    In the Shared Switch virtualization mode, the stress test to load and unload the GPU driver on Guest VM in every 30 second interval runs into issues approximately after three hours of the test.

    Workaround

    Do not run the stress reload driver cycle at this time.

  • A few Async SMBPBI commands do not function as intended when the driver is unloaded.

    When the driver is unloaded, the following Async SMBPBI commands do not operate as specified:

    • Arg1 0x00: Reads total GPU power limit control data.
    • Arg1 0x01: Sets the total GPU power limit.
    • Arg1 0x02: Reads the total GPU power limit policy information.
    Due to this issue, some properties of the following Redfish URIs are impacted:
    • PowerLimitWatts.SetPoint:

      /redfish/v1/Systems/HGX_Baseboard_0/Processors/GPU_SXM_[1-8]/EnvironmentMetrics

    • SpeedLimitMHz, SpeedLocked:

      /redfish/v1/Systems/HGX_Baseboard_0/Processors/GPU_SXM_[1-8]

    The Patch operation of the following URIs are impacted:
    • PowerLimitWatts.SetPoint:

      /redfish/v1/Systems/HGX_Baseboard_0/Processors/GPU_SXM_[1-8]/EnvironmentMetrics

    • Oem.Nvidia.PowerMode "MaxP" or "MaxQ":

      /redfish/v1/Chassis/HGX_Chassis_0/EnvironmentMetrics

    • SpeedLimitMHz, SpeedLocked:

      /redfish/v1/Systems/HGX_Baseboard_0/Processors/GPU_SXM_[1-8]

    Workaround

    Load the driver for these URIs to work properly.

  • Fabric Manager state is not reported accurately on NVSwitch OOB query

    The NVSwitch SMPBI query that reports Fabric Manager state (Manager State) is not reporting the actual FM state.

  • Instructions to reset all GPUs Using the nvidia-smi -r Command

    When resetting all GPUs using the nvidia-smi command with the -r option instead of a resetting specific GPU using the -i <gpu_index> option, all the NVSwitches will also be reset. This process wipes out the NVSwitch routing entries, and subsequent CUDA application launches will fail. The Fabric Manager service will also show interaction errors with the NVSwitch device via the switch driver.

    Workaround
    1. Stop the Fabric Manager service.
    2. To reset all GPUs, run nvidia-smi -r.
    3. After the reset is finished, start the Fabric Manager service.

GPU Performance Counters

The use of developer tools from NVIDIA that access various performance counters requires administrator privileges. See this note for more details. For example, reading NVLink utilization metrics from nvidia-smi (nvidia-smi nvlink -g 0) would require administrator privileges.

NoScanout Mode

NoScanout mode is no longer supported on NVIDIA Data Center GPU products. If NoScanout mode was previously used, then the following line in the “screen” section of /etc/X11/xorg.conf should be removed to ensure that X server starts on data center products:

Option         "UseDisplayDevice" "None"

NVIDIA Data Center GPU products now support one display of up to 2560x1600 resolution.

Unified Memory Support

CUDA and unified memory is not supported when used with Linux power management states S3/S4.

IMPU FRU for Volta GPUs

The driver does not support the IPMI FRU multi-record information structure for NVLink. See the Design Guide for Tesla P100 and Tesla V100-SXM2 for more information.

OpenCL 3.0 Known Issues

Device side enqueue

Confidential Compute

  • Confidential Compute Early Access has concluded, and CC support is removed in this, and all future r535 releases.
  • Confidential Compute General Access is now enabled in the r550 version and future releases.
  • For more information, please visit https://docs.nvidia.com/nvtrust.

2. Virtualization

To make use of GPU passthrough with virtual machines running Windows and Linux, the hardware platform must support the following features:

  • A CPU with hardware-assisted instruction set virtualization: Intel VT-x or AMD-V.

  • Platform support for I/O DMA remapping.

  • On Intel platforms, the DMA remapper technology is called Intel VT-d.

  • On AMD platforms, it is called AMD IOMMU.

Support for these features varies by processor family, product, and system, and should be verified at the manufacturer's website.

The following hypervisors are supported for virtualization:

Hypervisor Notes
Citrix XenServer Version 6.0 and later
VMware vSphere (ESX / ESXi) Version 5.1 and later.
Red Hat KVM Red Hat Enterprise Linux 7 with KVM
Data Center products now support one display of up to 2560x1600 resolution.

The following GPUs are supported for device passthrough for virtualization:

GPU Family Boards Supported
NVIDIA Ada Lovelace NVIDIA L40, L4
NVIDIA Grace Hopper NVIDIA GH200
NVIDIA Hopper NVIDIA H100, NVIDIA H800
NVIDIA Ampere GPU Architecture NVIDIA A800, A100, A40, A30, A16, A10, A10G, A2, AX800
NVIDIA Turing NVIDIA T4, NVIDIA T4G
NVIDIA Volta NVIDIA V100
NVIDIA Pascal

Quadro: P2000, P4000, P5000, P6000, GP100

Tesla: P100, P40, P4

NVIDIA Maxwell

Quadro: K2200, M2000, M4000, M5000, M6000, M6000 24GB

Tesla: M60, M40, M6, M4

3. Hardware and Software Support

Support for these features varies by processor family, product, and system, and should be verified at the manufacturer's website.

Supported Operating Systems for NVIDIA Data Center GPUs

The Release 535 driver is supported on the following operating systems:

  • The following table summarizes the supported Linux 64-bit distributions. For a complete list of distributions, kernel versions supported, see the CUDA Linux System Requirements documentation.

    Distribution x86_64 POWER*** Arm64 Server
    Debian 11.x (where x <= 9)** Yes No No
    Debian 10. x (where x <= 13)** Yes No No
    OpenSUSE Leap 15.x (where y <= 6) Yes No No
    Fedora 37** Yes No No
    Red Hat Enterprise Linux 9.y (where y <= 4) Yes No Yes
    Rocky Linux 9.y (where y <= 4) Yes No No
    Red Hat Enterprise Linux 8.y (where y <= 10) Yes Yes Yes
    Rocky Linux 8.y (where y <= 10) Yes No Yes
    Red Hat Enterprise Linux / CentOS 7.y (where y <= 9) Yes No No
    SUSE Linux Enterprise Server 15.y (where y <= 6) Yes No Yes
    Ubuntu 22.04.z LTS (where z <= 4) Yes No Yes
    Ubuntu 20.04.z LTS (where z <= 6) Yes No Yes
    KylinOS V10 SP2 Yes No No
    CBL-Mariner 2.0* Yes No No

    * CBL-Mariner will be supported by TRD via runfile. CUDA Toolkit will not support this OS as this is a deployment OS.

    ** Deprecated. Support will be removed in the next release.

    *** This is the last R535 TRD to support PPC. The following TRDs (posted after 2024) will no longer have PPC support.

Supported Operating Systems and CPU Configurations for NVIDIA HGX H20

  • Hopper Linux distributions:

    • Red Hat Enterprise Linux 9.3

    • Ubuntu 22.04 with NVIDIA HWE kernel

Supported Operating Systems and CPU Configurations for NVIDIA HGX GH200

  • Grace Hopper Linux distributions:

    • Red Hat Enterprise Linux 9.3

    • SUSE Linux Enterprise Server 15 SP5 QU1
    • Ubuntu 22.04 with NVIDIA HWE kernel

      RHEL and SLES feature parity with NVIDIA HWE Kernels. The latest RHEL 9 and SLES 15 SP5 kernels support bare metal.

Supported Operating Systems and CPU Configurations for NVIDIA HGX H100/H800

The Release 535 driver is validated with NVIDIA HGX H100 on the following operating systems and CPU configurations:

  • Linux 64-bit distributions:

    • Red Hat Enterprise Linux 8.9 (in 4/8/16-GPU configurations)

    • Red Hat Enterprise Linux 9.3 (in 4/8/16-GPU configurations)
    • SUSE Linux Enterprise Server 15.5 (in 4/8/16-GPU configurations)
    • Ubuntu 22.04.4 LTS (in 4/8/16-GPU configurations)

Supported Operating Systems and CPU Configurations for NVIDIA HGX A100/A800

The Release 535 driver is validated with NVIDIA HGX A100 on the following operating systems and CPU configurations:

  • Linux 64-bit distributions:

    • Debian 11.9
    • Debian 10.13
    • Red Hat Enterprise Linux 8.9 (in 4/8/16-GPU configurations)

    • Red Hat Enterprise Linux 7.9 (in 4/8/16-GPU configurations)

    • Rocky Linux 8.8 (in 4/8/16-GPU configurations)

    • Red Hat Enterprise Linux 9.3 (in 4/8/16-GPU configurations)
    • CentOS Linux 7.9 (in 4/8/16-GPU configurations)

    • Ubuntu 22.04.4 LTS (in 4/8/16-GPU configurations)

    • Ubuntu 20.04.6 LTS (in 4/8/16-GPU configurations)

    • SUSE SLES 15.5 (in 4/8/16-GPU configurations)

    • KylinOS V10 SP2

Supported Virtualization Configurations

The Release 535 driver is validated with NVIDIA HGX A100, HGX A800, H100, and H800 on the following configurations:

  • Passthrough (full visibility of GPUs and NVSwitches to guest VMs):

    • 8-GPU configurations with Ubuntu 20.04.6 and 22.04.4

  • Shared NVSwitch (guest VMs only have visibility of GPUs and full NVLink bandwidth between GPUs in the same guest VM):

    • 1/2/4/8/16-GPU configurations with Ubuntu 20.04.6 LTS

API Support

This release supports the following APIs:

  • NVIDIA® CUDA® 12.2 for NVIDIA® MaxwellTM, PascalTM, VoltaTM, TuringTM, HopperTM, NVIDIA Ampere architecture, and NVIDIA Ada Lovelace GPU architecture GPUs

  • OpenGL® 4.6

  • Vulkan® 1.3

  • DirectX 11

  • DirectX 12 (Windows 10)

  • Open Computing Language (OpenCLTM software) 3.0

Note that for using graphics APIs on Windows (such as OpenGL, Vulkan, DirectX 11, and DirectX 12) or any WDDM 2.0+ based functionality on Data Center GPUs, vGPU is required. See the vGPU documentation for more information.

Supported NVIDIA Data Center GPUs

The NVIDIA Data Center GPU driver package is designed for systems that have one or more Data Center GPU products installed. This release of the driver supports CUDA C/C++ applications and libraries that rely on the CUDA C Runtime and/or CUDA Driver API.

Attention: Release 470 was the last driver branch to support Data Center GPUs based on the NVIDIA Kepler architecture. This includes discontinued support for the following compute capabilities:
  • sm_30 (NVIDIA Kepler)
  • sm_32 (NVIDIA Kepler)
  • sm_35 (NVIDIA Kepler)
  • sm_37 (NVIDIA Kepler)
For more information on GPU products and compute capability, see https://developer.nvidia.com/cuda-gpus.
NVIDIA Server Platforms
Product Architecture
NVIDIA HGX H20-3e 8-GPU H20 and NVSwitch
NVIDIA HGX H20 8-GPU H20 and NVSwitch
NVIDIA HGX H100 8-GPU H100 and NVSwitch
NVIDIA HGX H800 8-GPU H800 and NVSwitch
NVIDIA HGX H100 4-GPU H100 and NVLink
NVIDIA HGX A800 8-GPU A800 and NVSwitch
NVIDIA HGX A100 8-GPU A100 and NVSwitch
NVIDIA HGX A100 4-GPU A100 and NVLink
NVIDIA HGX-2 V100 and NVSwitch
Data Center L-Series Products
Product GPU Architecture
NVIDIA L2 NVIDIA Ada Lovelace
NVIDIA L20 NVIDIA Ada Lovelace
NVIDIA L40 NVIDIA Ada Lovelace
NVIDIA L40S NVIDIA Ada Lovelace
NVIDIA L4 NVIDIA Ada Lovelace
Data Center H-Series Products
Product GPU Architecture
NVIDIA H100 PCIe NVIDIA Hopper
NVIDIA H100 NVL NVIDIA Hopper
NVIDIA H800 PCIe NVIDIA Hopper
NVIDIA H800 NVL NVIDIA Hopper
RTX-Series / T-Series Products
Product GPU Architecture
NVIDIA RTX 6000 Ada Generation NVIDIA Ada Lovelace
NVIDIA RTX 4000 SFF Ada Generation NVIDIA Ada Lovelace
NVIDIA RTX A6000 NVIDIA Ampere architecture
NVIDIA RTX A5000 NVIDIA Ampere architecture
NVIDIA RTX A4000 NVIDIA Ampere architecture
Quadro RTX 8000 NVIDIA Turing
Quadro RTX 6000 NVIDIA Turing
Quadro RTX 4000 NVIDIA Turing
NVIDIA T1000 NVIDIA Turing
NVIDIA T600 NVIDIA Turing
NVIDIA T400 NVIDIA Turing
Data Center A-Series Products
Product GPU Architecture
NVIDIA A2 NVIDIA Ampere architecture
NVIDIA A800, AX800 NVIDIA Ampere architecture
NVIDIA A100X NVIDIA Ampere architecture
NVIDIA A100

NVIDIA A100 80 GB PCIe

NVIDIA Ampere architecture
NVIDIA A40 NVIDIA Ampere architecture
NVIDIA A30. A30X NVIDIA Ampere architecture
NVIDIA A16 NVIDIA Ampere architecture
NVIDIA A10, A10M, A10G NVIDIA Ampere architecture
Data Center T-Series Products
Product GPU Architecture
NVIDIA T4, T4G NVIDIA Turing
Data Center V-Series Products
Product GPU Architecture
NVIDIA V100 Volta
Data Center P-Series Products
Product GPU Architecture
NVIDIA Tesla P100 NVIDIA Pascal
NVIDIA Tesla P40 NVIDIA Pascal
NVIDIA Tesla P4 NVIDIA Pascal
Data Center M-Class Products
Product GPU Architecture
NVIDIA Tesla M60 Maxwell
NVIDIA Tesla M40 24 GB Maxwell
NVIDIA Tesla M40 Maxwell
NVIDIA Tesla M6 Maxwell
NVIDIA Tesla M4 Maxwell

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