GLM-5.3-224E-FP8-MAN

GLM-5.3 224E FP8 MAN

This is an independently pruned FP8 checkpoint derived from zai-org/GLM-5.3. It retains 224 of the original 256 routed experts in every sparse layer while keeping top-8 routing unchanged. Here, 224E means 224 routed experts per sparse layer; it does not mean 224 billion parameters.

The checkpoint is not an official Z.ai release. The original tokenizer, chat template, dense weights, attention weights, shared experts, and license are retained. Only the routed-expert inventory and corresponding router outputs are rewritten according to the keep map.

Pruning Method

Experts were selected with Mean Activation Norm (MAN):

S(1,0,1) = mean(l2(unweighted_expert_output)) over tokens routed to the expert

For each sparse layer, the 224 experts with the largest MAN scores are retained. No retraining, fine-tuning, merging, or weight averaging is applied. Seven activation-outlier candidates in layer 77 were protected with the conservative guardrail max_abs > max(global_p99.5, global_max / 10). The MTP layer reuses the layer-77 keep IDs.

This reduces the routed-expert count by 12.5%, from 256 to 224, while preserving top-8 routing.

Calibration Data

The deterministic calibration subset contains 2,848 samples and 1,983,722 tokens, with approximately 500,000 tokens per axis:

The source pool used public data from:

• allenai/c4 (en)
• theblackcat102/evol-codealpaca-v1
• open-r1/Mixture-of-Thoughts (code, math, and science)
• glaiveai/glaive-function-calling-v2
• SWE-bench/SWE-smith-trajectories (tool)

The calibration data is not included in this model repository. The subset was selected with seed 42, without packing or truncation, and scored across eight Gaudi2 ranks. Its SHA-256 is 27bdf438f05019d65655c2f0db7ef0b7f994d3c2b250abe66244f30af986a372.

Validation

The materialized checkpoint passed deep structural and tensor validation:

The GPQA run used the public OpenAI simple-evals GPQA Diamond CSV with one deterministic permutation per question, seed 0, temperature=1.0, top_p=0.95, and a maximum output of 120,000 tokens in a 131,072-token context. The clustered bootstrap 95% interval was 92.5%-100%. This is a 40-question evaluation, not a claim of performance on the complete benchmark or under the unpublished Z.ai evaluation protocol.

The checkpoint was runtime-qualified with vLLM on eight Intel Gaudi2 devices using tensor parallelism 8, expert parallelism 8, BF16 KV cache, and a 131,072-token maximum context. In that environment it produced 19.72 tokens/s single-stream and 237.03 aggregate tokens/s across 16 concurrent 1,024-token generations. These throughput figures are hardware- and configuration-specific.

See prune-report.json for hashes and machine-readable provenance.

Usage

Use a recent Transformers or vLLM release with GLM-5.3, FP8 MoE, and the target accelerator supported. Follow the upstream zai-org/GLM-5.3 chat-template and serving guidance. In particular, GLM-5.3 supports reasoning_effort values low, high, and max; benchmark validation used the default max behavior.

Deployment has been validated on Gaudi2 with TP8/EP8. CUDA and other backends were not qualified as part of this release.

Limitations

• This is a 12.5% expert-pruned derivative and may regress on tasks not covered by the calibration and evaluation suites.
• Calibration was task-agnostic but English-heavy; no explicit Spanish calibration data was used.
• GPQA coverage is limited to a deterministic 40-question subset.
• Long-context, multilingual, safety, coding-agent, and tool-use behavior has not been exhaustively benchmarked.
• The original GLM-5.3 license applies. Review LICENSE before use.

Method References

• Unified expert-pruning formulation, MAN, and MSAN: https://arxiv.org/abs/2606.15716v1
• Reference implementation: https://github.com/ZongfangLiu/unified-expert-pruning
• Activation-outlier motivation: https://arxiv.org/abs/2507.23279v3

For the base model architecture, capabilities, citation, and intended use, see the upstream zai-org/GLM-5.3 model card.