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      "body": "Is there any plan for Quantized model (GGUF or others )?\n\nAs  designed for lower hardware from the beginning, supporting for Quantized format will make  the project more suitable for  production environment. ",
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      "body": "Thanks for the great work.\nMay i know if it runs on arm machine such as Jetson orin nano ?\nregards,",
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      "title": "[design question] why get rid of mmap?",
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      "body": "Thanks for the library! Really great work to see large models running on consumer devices. :D\n\nI'm curious, why get rid of mmap? Is the kernel page cache really that much less efficient than rolling your own?\n\nMemory allocated with mmap can be evicted from the page cache since it's backed by disk, but memory used with open()/read() must be kept around, so I don't understand why the README says you get lower RAM usage without it. With techniques like madvise(), I expect mmap should have been better on low-end and high-end devices.\n\nWhen llama-cpp switched to mmap-based weight loading in 2023, they found it to be much faster and simpler, even on low-memory devices. See Justine Tunney’s tech report for motivation , https://justine.lol/mmap/ and the llama-cop comments + commit by @jart, https://github.com/ggml-org/llama.cpp/issues/91",
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      "body": "Is it possible to configure custom resource limits? My system has 64GB RAM + 8GB GPU, but I'd like to cap usage at 40GB RAM and 7.5GB GPU. When running multiple models (2-3 instances), they should prioritize GPU allocation, then use RAM, with SSD as the final resort.",
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      "body": "Hello!\nAny plans for mac? M4 is so nice these days.",
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      "body": "ssd slow, ram fast. 196g-256g ram is available to consumers.",
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