Kimi-K3-Audit-451E-MLX-MXFP4

Kimi-K3-Audit-451E-MLX-MXFP4

Kimi-K3-Audit-451E is Kimi K3 specialised for security auditing. It keeps 451 of the 896 routed experts in each layer, which brings the model onto two 512 GB Apple silicon machines at half the size of the release. On kernel-audit workloads it measures at parity with the full model. Expert selection was calibrated on kernel source and audit traces, targeting domain focus rather than compression.

For a single 512 GB machine, use the smaller Kimi-K3-Audit-240E build instead.

Intended use

Security auditing and analysis of source code, with tool use. General conversation, translation and non-technical writing are out of scope, and a general-purpose model should be used for them.

Across eight code corpora in five languages, pruning changes perplexity by at most 1.10×, against 1.38× for the 240E build.

Evaluation

Measured against the original MXFP4 release (896E) on identical tokens. KLD is KL(896E ‖ 451E) over the full output distribution.

Audit traces

Each of 24 kernel-audit sessions contributes one 1025-token window, rendered through the model's own chat template so the structure matches deployment.

Overall perplexity on audit traces is slightly below the release. Tool-call spans diverge least of any span measured. 81% of tool-call tokens fall below 0.01 divergence, against 24% of thinking tokens.

Code and prose corpora

Build comparison

Usage

This model requires Kimi K3 support from mlx-lm PR #1626, which has not yet been merged. Until it lands in an mlx-lm release, install from the PR branch on both machines.

pip install git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1626/head
pip install tiktoken

The model does not fit on one 512 GB machine. Serve it across two with tensor parallelism, using an MLX distributed hostfile for the pair. A fast interconnect matters, Thunderbolt RDMA works well.

mlx.launch --backend jaccl --hostfile hosts.json --env MLX_METAL_FAST_SYNCH=1 -- \
  mlx_lm.server --model catalystsec/Kimi-K3-Audit-451E-MLX-MXFP4

Prefill memory scales with chunk × context and is separate from the weights, so very long prompts may need a smaller --prefill-step-size than the default 2048.

Sampling follows the base model, temperature 1.0 and top-p 0.95.

Limitations

Evaluation is teacher-forced throughout and measures next-token prediction on reference text. Generation-time behaviour, including loop rate and stop-token reliability, was not evaluated.

Standard downstream benchmarks were not evaluated.

License

Inherits the Kimi K3 license from the base model.