---
license: other
base_model: cyankiwi/GLM-5.3-AWQ-INT4
base_model_relation: quantized
pipeline_tag: text-generation
tags:
  - glm
  - glm-5.3
  - moe
  - w4a16
  - awq
  - int4
  - compressed-tensors
  - reap
  - expert-pruning
  - hopper
---

> [!TIP]
> **[Support this work →](https://donate.sybilsolutions.ai)** · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/cerebras-reap)

# GLM-5.3-569B — REAP keep-192 (W4A16 / INT4)

**GLM-5.3 with 25% of its experts removed, at INT4 — 372 GB, built to serve on 4× H200 (Hopper) in vLLM or SGLang.**

## What this is

GLM-5.3 is a 753B mixture-of-experts model: each token uses 8 of 256 expert sub-networks per layer (~40B active). **REAP** (Router-weighted Expert Activation Pruning) scores each expert’s real contribution and deletes the least useful ones — no retraining. This cut keeps **192 of 256** experts per layer.

The experts are then INT4 **W4A16** (compressed-tensors, AWQ), taken from the [cyankiwi/GLM-5.3-AWQ-INT4](https://huggingface.co/cyankiwi/GLM-5.3-AWQ-INT4) base. Only the routed experts are 4-bit; attention, the shared expert, the dense layers, and the head stay BF16. vLLM auto-selects the Marlin MoE kernel.

## Which one, and what about Blackwell?

| Variant | Experts kept | Size | KL vs BF16 |
|---|---|---|---|
| [504B](https://huggingface.co/0xSero/GLM-5.3-504B-W4A16) | 168 / 256 | 328 GB | 0.506 |
| **569B** (this) | 192 / 256 | 372 GB | 0.357 |

This W4A16 build is the **Hopper-servable** sibling of the EXL3 series. On Blackwell or consumer GPUs use the EXL3 equivalent instead — [`GLM-5.3-569B-EXL3-3.0bpw`](https://huggingface.co/0xSero/GLM-5.3-569B-EXL3-3.0bpw), which measures the same fidelity (0.361).

## How close to the original is it?

**KL divergence vs full BF16: 0.357 nats** (sealed 25-prompt panel, full 154k vocabulary). KL is the standard “how differently do these two models predict” score — **0 = identical**, lower = closer. Notably this is essentially the same as the EXL3 cut at the same expert count (0.361): **the pruning drives the fidelity, the quant format barely moves it.** Full numbers: [fidelity study](https://huggingface.co/datasets/0xSero/glm-5.3-reap-fidelity-study).

## Why these experts

Instead of keeping the globally most-frequent experts (which deletes a domain’s specialists), each expert is scored by its **largest share of any single domain’s routed work**, so every domain — code, rare languages, structured output — keeps its specialists. Head-to-head vs frequency pruning: [fidelity study](https://huggingface.co/datasets/0xSero/glm-5.3-reap-fidelity-study).

## Serving (vLLM, 4× H200)

```bash
vllm serve 0xSero/GLM-5.3-569B-W4A16 --tensor-parallel-size 4 --trust-remote-code --max-model-len 131072
```

## Credits

- **[Z.AI / zai-org](https://huggingface.co/zai-org)** — [GLM-5.3](https://huggingface.co/zai-org/GLM-5.3), the base model.
- **[cyankiwi](https://huggingface.co/cyankiwi)** — the [GLM-5.3-AWQ-INT4](https://huggingface.co/cyankiwi/GLM-5.3-AWQ-INT4) W4A16 base this prune is built on.
- **[Cerebras Research](https://github.com/CerebrasResearch/reap)** — REAP ([arXiv:2510.13999](https://arxiv.org/abs/2510.13999)).

Observations: [`glm-5.3-reap-observations-v1`](https://huggingface.co/datasets/0xSero/glm-5.3-reap-observations-v1) · Fidelity study: [`glm-5.3-reap-fidelity-study`](https://huggingface.co/datasets/0xSero/glm-5.3-reap-fidelity-study) · Built on 8× NVIDIA RTX PRO 6000 Blackwell.

## License
Inherits the [GLM-5.3 license](https://huggingface.co/zai-org/GLM-5.3).