CUDA Toolkit 11.3

eol
Released
2021-04 (approximate — Phoronix/DEVCLASS coverage dates the 11.3.0 GA to mid-April 2021, not independently confirmed from a single authoritative NVIDIA source)
Minimum driver — Linux
465.19.01
Minimum driver — Windows
465.89
Supported GPU architectures
MaxwellPascalVoltaTuringAmpere
Compute capability (sm_XX)
sm_50sm_52sm_53sm_60sm_61sm_62sm_70sm_72sm_75sm_80sm_86
Commercial GPU names
GTX 750 Ti / Tesla M-seriesGTX 900 series / Tesla M60Jetson TX1Tesla P100GTX 10 series / Tesla P40Jetson TX2Tesla V100Jetson AGX XavierRTX 20 series / Tesla T4A100 / A30RTX 30 series / A40
Compatible cuDNN
8.x

WSL2 & Docker

WSL2: this version's own driver minimum above (Linux 465.19.01 / Windows 465.89) is below the R495 driver branch NVIDIA's CUDA-on-WSL2 guide requires for GPU support under WSL2 (docs.nvidia.com/cuda/wsl-user-guide) -- meeting the minimum in this entry is not enough to get this CUDA version working inside WSL2; install a Windows driver from the R495 branch or later instead, and never install a separate Linux driver inside WSL2 itself (the Windows driver is exposed into WSL2 automatically). Docker: a host driver >= the Linux minimum above plus the NVIDIA Container Toolkit is sufficient to run this CUDA version in a container -- no extra Docker-specific driver floor beyond that number.

Added 2026-09 to fix a dead cross-link: adding PyTorch 1.12.0 (which lists CUDA 11.3 as one of its official wheel builds) would otherwise link to a nonexistent page, the same gap this site's 10.2/11.2/11.6 entries were each added to close for other PyTorch/TensorFlow versions. Driver minimums (Linux 465.19.01 / Windows 465.89) confirmed via NVIDIA's CUDA Toolkit release notes (Table 3), consistent across the 11.3.0 GA and 11.3.1 update. Architecture/compute-capability list carried over unchanged from the 11.2/11.6 entries (same Maxwell-through-Ampere range) rather than independently re-verified against NVIDIA's per-version architecture matrix for 11.3 specifically.

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