Modelpublic

spicyneuron/Kimi-K2.7-Code-MLX-3.6bit

sourceHugging Faceupdated 3mo agoView on Hugging Face

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Model Card

moonshotai/Kimi-K2.7-Code optimized for running on a Mac Studio M3 Ultra.

—A mixed-precision quant that balances speed, memory, and accuracy.
—3-bit MoE baseline with important always-on layers at higher precision.
—Fits into ~460 GB memory, leaving enough room for a smaller utility model.

Usage

# Start server at http://localhost:8080/v1/chat/completions
uvx --from mlx-lm mlx_lm.server \
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/Kimi-K2.7-Code-MLX-3.6bit

Benchmarks

metric	this model
bpw	3.578
base memory	427.579
peak memory (1024/512)	460.444
prompt tok/s (1024)	218.851 ± 0.208
gen tok/s (512)	21.035 ± 0.049
perplexity	4.462 ± 0.037
arc_challenge	0.692 ± 0.021
hellaswag	0.780 ± 0.019

Methodology

Quantized with a mlx-lm fork. MLX quantization options differ than llama.cpp, but the principles are the same:

—Sensitive layers like MoE routing, attention, and output embeddings get higher precision
—More tolerant layers like MoE experts get lower precision