Kimi K2.7 Code (1T) has been compressed into a 325 GB GGUF via Dynamic 2-bit quantization that upcasts critical layers, cutting model size by 48%. Unsloth AI says the quantized build achieves over 40 tokens/sec on ~330 GB RAM/VRAM setups, while a full-precision run needs about 610 GB; download and guide links are provided.
Kimi K2.7 (1T) was quantized to a 325 GB GGUF using 'Dynamic 2-bit' with selective upcasting of important layers, a 48% size reduction from the original
You need 325GB VRAM to run Kimi K2.7 locally…
Unsloth AI (@UnslothAI)
You can now run Kimi K2.7 Code locally! 🌘
We shrank the 1T model to 325GB (-48%) via Dynamic 2-bit where important layers are upcasted.
Run at >40 tok/s on 330GB RAM/VRAM setups.
Run full precision on 610 GB.
Guide: unsloth.ai/docs/models/kimi-…
GGUF: huggingface.co/unsloth/Kimi-…
— https://nitter.net/UnslothAI/status/2066492839450800427#m