• 15 Posts
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Joined 1 month ago
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Cake day: July 29th, 2026

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  • Well the hashes are always much smaller than the actual data, otherwise downloading the hashes would take just as long as downloading the data lol.

    So 16KB blocks means 131072 bits per block, subtract the 256 bit hash size and that means 2^130816 hash collisions

    That’s a lot lol, you’ll never find which one is the correct one. Even if you instantly had a list of all values that match the desired hash, you’d have to check them all and somehow determine which one was correct.











  • Number of Parameters: 125B with 6B activated, plus 51B n-gram embedding and 4B MTP

    I bet you could run this in Q4 on 96GB RAM and 16GB VRAM, maybe even less. The benchmark scores seem good, beating 27b and DeepSeek Flash.

    The n-gram embeddings sound very similar to Gemma 4 e4b embeddings. Need llama.cpp to support streaming n-grams from SSD, mmap would be less efficient than having explicit support.

    GGUFs are starting to be available now








  • 8GB VRAM RTX 3070 + 32 GB DDR5 + 1TB NVMe SSD - AMD Ryzen 5 7600X CPU - putting this here in case it’s substantially better than the BC-250

    Yes this can run Qwen 3.6 35b-a3b pretty nicely! And they might be releasing an updated version of that soon. Your BC-250 only has 16GB total which is not enough for 35b.

    I also have 32GB RAM and 8GB VRAM, my computer is a little slower than yours, see my guide: https://lemmus.org/post/24235317

    For the BC-250 you might try smaller models like Ling 3.0 Tiny, Ornith 1.5 9b, or Gemma 4 12b QAT

    For your 8GB RAM devices, you can run Gemma 4 e4b QAT, Qwen 3.5 4b, or maybe Ling 3.0 Tiny

    I’m a noob to local LLMs.

    Use Unsloth Desktop or llama.cpp. Then you can connect Zoo Code to it, that’s a VSCode extension which I like for programming with my local LLMs.