@wavaai.feeds: 1 Trillion Parameters. On Your Desk. Fully local, no API, no cloud leash. Powered by a 1T MoE hybrid. 🤯 With 256K context, this has been quantized to an impressive 240GB. You can now run state-of-the-art-level thinking on serious rigs. The original 600GB beast? Now crushed to 240GB with Unsloth's Dynamic ~1.8-bit quantization. That's a -60% size reduction while still maintaining SOTA reasoning, vision, and agents. ### Hardware Reality Check This is not your grandma’s laptop. Local AI now means having serious hardware: - **Target:** Combined RAM + VRAM > 240GB for decent speed. - **Mac Studio (256GB unified):** Smooth performance at over 10 tokens/s. - **Beefy multi-GPU rigs:** Capable of reaching 40+ tokens/s. - **Smaller setups:** Runs via llama.cpp offloading but expect <2 tok/s crawl. ### Key Specs - **Architecture:** Hybrid MoE (1T total, 32B active). - **Sweet spot quant:** 2-bit UD-Q2_K_XL (~375GB) for a killer balance of size vs. logic sharpness. - **Context:** 256K tokens - insane! ### Pro Inference Tips Want maximum reasoning without loops? Enable **Thinking mode** with: - Temperature: 1.0 - Min_P: 0.01 Frontier intelligence is migrating from locked data centers straight to your rig. If you’ve got the memory, you own the model! - [huggingface.co/unsloth/Kimi-K…](https://huggingface.co/unsloth/Kimi-K…)
