Note: Quality checks still in progress

Model Description

GLM-5.2-NVFP4 is an NVFP4-quantized version of zai-org/GLM-5.2, a 744B-parameter Mixture-of-Experts language model with 40B active parameters, 256 experts per MoE layer (8 activated per token), and DeepSeek Sparse Attention (DSA).

Quantized directly from the full BF16 checkpoint (zai-org/GLM-5.2, not the FP8 release, to NVFP4 (4-bit with blockwise FP8 scales per 16 elements) using NVIDIA Model Optimizer.

What's quantized

Only the non-shared MoE expert MLP projections are quantized to NVFP4. Attention weights are left in BF16, in addition to the dense MLPs (layers 0-3) and the shared experts. Since the MoE expert weights constitute the vast majority of model parameters in an MoE architecture, this still yields significant memory savings.

Calibration uses natural top-k routing rather than forcing all experts to activate, so each expert's quantization scales reflect the token distributions it actually sees during inference. To compensate, calibration was run on a much larger number of samples than typical to ensure broad expert coverage through natural routing alone.

Calibration dataset

Three calibration passes were run:

1. Coding pass — Agentic coding samples (tool calling, multi-turn code generation, function calling) with English and Chinese system prompts.
2. Broad pass — Large-scale diverse samples drawn from WildChat-NonToxic and LMSYS-Chat covering real user conversations across a wide range of topics and languages.
3. Deep pass — Long-context samples (>8K tokens) from coding and diverse sources to exercise deep-sequence expert activation patterns.

Requirements

Hardware: 8x RTX PRO 6000 Blackwell 96GB (b12x MoE runner recommended)

Community Testing

Base model