{
  "video_id": "reddit_1tp0vk1",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "Info: Nvidia Cuda 13.3 landed",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1tp0vk1/info_nvidia_cuda_133_landed/",
  "external_url": null,
  "upload_date": "20260527",
  "published_at": "2026-05-27T09:53:24+00:00",
  "transcript": "[Cuda 13.3 Downloads](https://developer.nvidia.com/cuda-downloads)\n\n[Release Notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html)\n\nAnybody already tried llama.cpp with 13.3?\n\n\n\n--- Top Comments ---\n\n\n[25 upvotes] Hopefully this has had better QA than 13.2\n\n[20 upvotes] Believe some guy from nvidia said in a llama.cpp issue that it should fix whatever problems 13.2 had with compiling llama.cpp\n\n[23 upvotes] Yeah, the bug from 13.2 is finally fixed.\n\n[21 upvotes] >▶ New Features\n\n>▶ Enabled memory-parsimonious tiling for FP64 emulated matrix multiplications. This improvement ensures that the workspace memory budget no longer exceeds 8 GB.\n\n>▶ Added support for CUDA Green contexts.\n\n>▶ Improved FP4 matrix multiplication performance on Blackwell Ultra GPUs by a geometric mean\nof 5% across a wide range of problems, with up to 7% speedup for some small problems.\n\n>▶ Improved TF32 matrix multiplication performance on Blackwell and Blackwell Ultra GPUs by a\ngeometric mean of 27% across a wide range of problems and layouts, with up to 3.5x speedup\nfor some small problems.\n\n>▶ Improved TF32 TN matrix multiplication performance on Hopper GPUs by a geometric mean of\n11% across a wide range of problems, with up to 40% speedup for some small problems.\n\n>▶ Improved SYMV performance with TMA-based acceleration for Hopper, Blackwell, and Blackwell\nUltra kernels.\n\n[7 upvotes] Nothing for my 3090s in it, most likely.",
  "transcript_chars": 1443,
  "ingested_at": "2026-05-27T13:30:04.505196+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 72,
    "upvote_ratio": 0.94,
    "num_comments": 18,
    "author": "parrot42",
    "is_self": true
  }
}