spicyneuron/Kimi-K2.5-MLX-2.8bit

Kimi K2.5 optimized to run comfortably on a Mac Studio M3 512G.

Other MLX options require 450G+, which is tight even with 500G of usable memory. This quant fits into ~380G with room to spare, giving you the flexibility to use longer contexts, run other models in parallel, and open up 3 browser tabs without OOM'ing.

If you're looking to use Kimi K2.5 as the core of a "Claude Code in a box" setup, you've come to the right place.

Update: Uploaded a v2 that improves perplexity while keeping the same size.

Update: Created an even smaller 2.5 bit version that uses less memory while maintaining the same perplexity as v1!

Usage

# Start server at http://localhost:8080/v1/chat/completions
uvx --from mlx-lm --with tiktoken \
  mlx_lm.server \
    --host 127.0.0.1 --port 8080 \
    --trust-remote-code \
    --model spicyneuron/Kimi-K2.5-MLX-2.8bit

# Kimi K2.5 requires tiktoken + remote code for the tokenizer

Methodology

Quantized with a mlx-lm fork, drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs. 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 (BF16, 8, 4)
• More tolerant layers like MoE experts get lower precision (2, 3)

This one is comparable to Unsloth's UD-Q2_K_XL in size, but loads and runs noticeably faster thanks to MLX. Compared to the 3 bit MLX, it's faster, uses 80G less memory, yet has lower perplexity.

Performance

Prompt Size	GGUF	MLX 3 bit	MLX 2.8 bit v1	MLX 2.8 bit v2	MLX 2.5 bit
1000	148.82	216.976	224.878	224.094	226.368
5000	130.90	230.227	235.595	231.966	237.426
10000	113.32	219.792	222.464	218.455	223.846
20000	89.72	186.549	187.915	186.169	188.502

Gen Size	GGUF	MLX 3 bit	MLX 2.8 bit v1	MLX 2.8 bit v2	MLX 2.5 bit
500	23.38	25.781	27.443	26.586	27.571
1000	22.37	25.210	26.491	24.285	26.853
2000	21.89	23.944	24.573	22.603	24.689
5000	20.52	20.758	21.030	20.499	21.192

Perplexity (MLX quants)

Model	Perplexity	Relative	Relative %
MLX 3 bit	3.798 ± 0.021	—	—
MLX 2.8 bit v1	3.768 ± 0.021	-0.030	-0.79%
MLX 2.8 bit v2	3.702 ± 0.020	-0.096	-2.53%
MLX 2.5 bit	3.777 ± 0.020	-0.021	-0.55%

# llama.cpp 8130
llama-bench -fa 1 --batch-size 2048 --ubatch-size 2048 --repetitions 5

# mlx_lm v0.30.7
mlx_lm.benchmark --num-trials 5
mlx_lm.perplexity --sequence-length 1000 --seed 222