New CRAM method offers giant boost to compressed memory reads(www.tomshardware.com)
41 pointsby danny008 hours ago6 comments
  • eglintondust6 hours ago
    So can I finally download more RAM?
    • smallmancontrovan hour ago
      https://downloadmoreram.com/

      Whoa, new options! I'm downloading the Quantum RAM with Haptic Feedback.

    • skavi33 minutes ago
      probably not to your current computer. this works on supporting memory types that do compression in hardware.
    • swed420an hour ago
      You wouldn't download a car
      • paimapi29 minutes ago
        has anybody tried 3D-printing a car yet
  • Kevin_Flynn8 hours ago
    Ram Doubler, for Mac and Win 3.x, 1996

    https://winworldpc.com/product/connectix-ram-double/windows-...

    Nice to see its back, with better performance.

    History repeated, with refinement.

  • cyberclimb3 hours ago
    the recording of the presentation has got to be on this YT channel but I only scrubbed through 2 videos (8h each) and it's not easy to find

    https://www.youtube.com/@LinuxPlumbersConference

    not sure where it falls in the schedule here either https://lpc.events/event/20/timetable/#all

  • vfosnar6 hours ago
    https://www.phoronix.com/news/Linux-CRAM-Compressed-RAM
    • dang2 hours ago
      Added above. Thanks!
    • x0z5 hours ago
      Thank you <3
  • cwillu6 hours ago
    The article appears to misunderstand the point of the project, and mostly just describes what zswap already does (writing compressed pages back into ram), rather than talking about cram's hardware-offloaded compression that allows cacheline-level access rather than page-level. (And they appear to be aware that they don't really understand it: “My explanation of CRAM might not be completely correct”)

    The phoronix article is better, and I say that as someone who usually detests the quality of phoronix's technical writing.

    • tancop3 hours ago
      It's not really about hardware offload, the biggest problem with zswap is that it's swap. The rest of the kernel treats it like a fast SSD (which is still incredibly slow compared to RAM) instead of slower memory that needs a bit of special handling on writes.

      NUMA maps a lot closer to what compressed RAM actually is. The subsystem is more aware of CRAMs specifics so it can make better decisions about where to put allocations and everything gets faster. And it's less overhead because swap is not really optimized for frequent direct access but for NUMA it's the most basic function.

  • madduci6 hours ago
    Finally we can run frontier models locally
    • srousseyan hour ago
      We brute force AI models right now because a) we don’t know better, and b) it’s premature optimization.

      I beg to differ on point b, but no one is delaying their next model just so they can concentrate on optimization.

      It’s coming though.

      One example: https://siliconangle.com/2026/07/28/ai-model-compression-sta...

      Another is separating the the intelligence part of the model from the known facts part of the model (which can be better compressed)

    • tintoran hour ago
      Compression doesn't really work for model weights.

      Model quantization and model distillation are two techniques to reduce model size.

    • throawayonthe4 hours ago
      one would hope model weights are already high entropy enough and would not be improved by a general-purpose memory compression algo?