GLM-5.1-MLX-2.9bit

GLM 5.1 optimized to run comfortably on a Mac Studio M3 512. This is the smaller, compact version. Alternatives: balanced, quality-first

• A mixed-precision quant that balances speed, memory, and accuracy.
• 3-bit baseline with important layers at 4, 8 and BF16.
• Fits into ~280 GB memory, leaving plenty of room to run parallel models (ex: Minimax M2.7, Qwen 3.6 35B).

Usage

# Start server at http://localhost:8080/chat/completions
uvx --from mlx-lm mlx_lm.server \
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/GLM-5.1-MLX-2.9bit

Benchmarks

* GLM 5.1 KL divergence calculated against the largest quant I could run locally (~495 GB), so real KL is higher.

Tested on a Mac Studio M3 Ultra with:

mlx_lm.kld --baseline-model path/to/mlx-full-precision
mlx_lm.perplexity --sequence-length 2048 --seed 123
mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 500

mlx_lm.kld is approximate, based on top_k not full logits. Here's the code.

Methodology

Quantized with a mlx-lm fork, drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs. MLX quantization options differ from 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