Unlock full RTX 5080 performance in PyTorch! PyTorch does not support RTX 5080 (sm_120) natively, so I built custom CUDA 12.8 drivers and PyTorch binaries to make it work. This repo contains build scripts, benchmarks, and installation guides for running AI models at max efficiency on the RTX 5080.
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Merge pull request #22 from kentstone84/claude/pytorch-cuda-compatibility-01PfmWHufyzmeUHUv1c45V4H
9dcbcbfView on GitHubAdd comprehensive evidence upload and community verification framework
104e13dView on GitHubAdd evidence framework: Timeline and screenshot organization structure
54c3f11View on GitHubMerge pull request #21 from kentstone84/claude/pytorch-cuda-compatibility-01PfmWHufyzmeUHUv1c45V4H
a2ccf99View on GitHubClean up: Move PROPOSED_README_UPDATE.md to docs, fix all links
1f58176View on GitHubOrganize documentation: Move all .md files to docs/ directory
fb11975View on GitHubMerge pull request #20 from kentstone84/claude/pytorch-cuda-compatibility-01PfmWHufyzmeUHUv1c45V4H
3ef4864View on GitHubAdd automated driver patching system - ready for Ghidra data
c0d6f50View on GitHubAdd FUN_005e0020: The function that actually handles sm_120 strings
c218685View on GitHubClarify function hierarchy: FUN_00f682d0 calls patched subfunctions
50f3550View on GitHubAdd Ghidra screenshot evidence and complete analysis documentation
a150a0cView on GitHubMerge pull request #19 from kentstone84/claude/pytorch-cuda-compatibility-01PfmWHufyzmeUHUv1c45V4H
16c0fadView on GitHubAdd verified benchmark results: 60.8% performance improvement
fd05b64View on GitHub