---
license: other
license_name: glm-5
license_link: https://huggingface.co/zai-org/GLM-5.1/blob/main/LICENSE
base_model:
- 0xSero/GLM-5.1-555B
tags:
- autoround
- expert-pruning
- glm
- gptq
- moe
- pruning
- reap
- vllm
- w4a16
library_name: transformers
pipeline_tag: text-generation
quantization_config:
  quant_method: gptq
  bits: 4
  group_size: 128
  sym: true
  desc_act: false
  checkpoint_format: gptq
base_model_relation: quantized
---

> [!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-5.1-555B-W4A16

W4A16 quantization of [0xSero/GLM-5.1-555B](https://huggingface.co/0xSero/GLM-5.1-555B).

## At a glance

| | |
|---|---|
| Base model | [0xSero/GLM-5.1-555B](https://huggingface.co/0xSero/GLM-5.1-555B) |
| Format | W4A16 |
| Total params | **555B** |
| Active / token | 14B |
| Experts / layer | 192 |
| Layers | 78 |
| Hidden size | 6144 |
| Context | 202,752 |
| On-disk size | 297 GB |

## Which variant should I pick?

| Variant | Format | Link |
|---|---|---|
| `GLM-5.1-444B` | BF16 | [link](https://huggingface.co/0xSero/GLM-5.1-444B) |
| `GLM-5.1-444B-GGUF` | GGUF | [link](https://huggingface.co/0xSero/GLM-5.1-444B-GGUF) |
| `GLM-5.1-478B-NVFP4` | NVFP4 | [link](https://huggingface.co/0xSero/GLM-5.1-478B-NVFP4) |
| `GLM-5.1-555B` | BF16 | [link](https://huggingface.co/0xSero/GLM-5.1-555B) |
| `GLM-5.1-555B-GGUF` | GGUF | [link](https://huggingface.co/0xSero/GLM-5.1-555B-GGUF) |
| `GLM-5.1-555B-NVFP4` | NVFP4 | [link](https://huggingface.co/0xSero/GLM-5.1-555B-NVFP4) |
| `GLM-5.1-555B-W4A16` **(this)** | W4A16 | [link](https://huggingface.co/0xSero/GLM-5.1-555B-W4A16) |

This is a **GPTQ 4-bit weight-quantized** variant of the 25% expert-pruned [`zai-org/GLM-5.1`](https://huggingface.co/zai-org/GLM-5.1) using [REAP](https://github.com/CerebrasResearch/reap) (Relative Expert Activation Pruning), produced with [AutoRound](https://github.com/intel/auto-round) for learned rounding optimization.

| Property | Value |
|----------|-------|
| Base model | `zai-org/GLM-5.1` (744B MoE, 256 experts/layer) |
| Architecture | `GlmMoeDsaForCausalLM` (MoE + Dynamic Sparse Attention) |
| Routed experts | 256 → 192 (25% removed, 64 per layer) |
| Active params/token | ~14B (top-8 routing preserved) |
| Quantization | GPTQ W4A16 (int4 symmetric, group_size=128) |
| Quantizer | auto-round 0.12.2 (200 iterations, SignSGD) |
| Quantized size | **277 GB** (56 safetensor shards) |
| BF16 source | [`0xSero/GLM-5.1-555B`](https://huggingface.co/0xSero/GLM-5.1-555B) |
| GGUF variant | [`0xSero/GLM-5.1-555B-GGUF`](https://huggingface.co/0xSero/GLM-5.1-555B-GGUF) (325 GB, Q4_K_M) |

## Benchmark Results (GGUF Q4_K_M, inference mode, temp=0.8)

The GPTQ W4A16 uses the same learned rounding method (AutoRound) as the GGUF Q4_K_M. Benchmark scores from the GGUF variant (zero repetition loops):

| Suite | Metric | Result | Repetition Loops |
|-------|--------|--------|-----------------|
| Terminal-Bench (50) | Proxy Pass | 44/50 (88%) | 0/50 |
| SWE-bench Pro (50) | Proxy Pass | 33/50 (66%) | 0/50 |
| GSM8K (50) | Correct | 30/50 (60%) | 0/50 |
| HLE (50) | Correct | 9/50 (18%) | 0/50 |

**Zero repetition loops across 220 benchmark probes.** The 25% prune retains 192/256 experts, providing enough expert diversity for stable generation at all sequence lengths.

## How to Use

### vLLM

```python
from vllm import LLM, SamplingParams

llm = LLM(
    model="0xSero/GLM-5.1-555B-W4A16",
    tensor_parallel_size=4,    # 4× B200 or 8× A100
    max_model_len=8192,
    trust_remote_code=True,
)

params = SamplingParams(temperature=0.8, max_tokens=4096)
outputs = llm.generate(["Hello, world!"], params)
```

### SGLang

```bash
python -m sglang.launch_server \
  --model-path 0xSero/GLM-5.1-555B-W4A16 \
  --tp 4 \
  --trust-remote-code
```

### Requires

- ~70-80 GiB VRAM per GPU across 4 GPUs (B200), or ~280 GiB total
- CUDA 12.8+ (sm_100a / Blackwell)
- vLLM >= 0.19.0 with `deep_gemm` installed (for DSA sparse attention)
- `trust_remote_code=True`

## Quantization Details

**Method:** AutoRound W4A16 — learned rounding via SignSGD (200 iterations per layer), calibrated on 128 samples from NeelNanda/pile-10k at 2048 sequence length.

**Protected (kept at full precision):**
- Dense MLP layers 0-2 (`gate_proj`, `up_proj`, `down_proj`)
- DSA indexer (`weights_proj`)
- `lm_head`

**Quantized to int4 (43,971/44,059 linear layers):**
- All attention projections (`q_a_proj`, `q_b_proj`, `kv_a_proj`, `kv_b_proj`, `o_proj`)
- All routed MoE expert projections (192 experts × gate/up/down × 75 MoE layers)
- Shared expert projections

**GPTQ config:** `bits=4, group_size=128, sym=true, desc_act=false`

## Why GPTQ over GGUF Q4_K_M?

| | GPTQ W4A16 (this) | GGUF Q4_K_M |
|---|---|---|
| Size | 277 GB | 325 GB |
| Serving | vLLM, SGLang, TGI (GPU) | llama.cpp (CPU/GPU hybrid) |
| Quant method | Learned rounding (SignSGD) | K-means clustering |
| Throughput | Higher (GPU-native kernels) | Lower |
| Best for | Production GPU serving | Local inference, edge |

GPTQ packs 4-bit weights more efficiently with `group_size=128` symmetric quantization, resulting in a smaller checkpoint than GGUF Q4_K_M at the same bit-width.

## Related Models

| Model | Prune % | Experts | Format | Size | Status |
|-------|---------|---------|--------|------|--------|
| [`0xSero/GLM-5.1-555B`](https://huggingface.co/0xSero/GLM-5.1-555B) | 25% | 192/256 | BF16 | 1.1T | Source checkpoint |
| [`0xSero/GLM-5.1-555B-GGUF`](https://huggingface.co/0xSero/GLM-5.1-555B-GGUF) | 25% | 192/256 | GGUF Q4_K_M | 325G | llama.cpp serving |
| **This model** | **25%** | **192/256** | **GPTQ W4A16** | **277G** | **vLLM/SGLang serving** |
| [`0xSero/GLM-5.1-444B`](https://huggingface.co/0xSero/GLM-5.1-444B) | 40% | 154/256 | BF16 | 910G | Has repetition issues — use 25% |

## Support This Work

If you find these models useful, please consider supporting continued open-source model compression research:

**[donate.sybilsolutions.ai](https://donate.sybilsolutions.ai)**

## License & citation
License inherited from the base model.

```bibtex
@misc{lasby2025reap,
  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}
```

## Sponsors
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