---
license: apache-2.0
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
  - qwen3
  - moe
  - nvfp4
  - quantized
  - nvidia-modelopt
  - coding
  - dgx-spark
model_type: qwen3_moe
quantized_by: kleinpanic93
pipeline_tag: text-generation
library_name: transformers
---

# Qwen3-Coder-30B-A3B-Instruct-NVFP4

NVFP4 (4-bit floating point) quantization of [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct), optimized for NVIDIA Blackwell GPUs.

## Model Details

| Property | Value |
|----------|-------|
| **Base Model** | Qwen/Qwen3-Coder-30B-A3B-Instruct |
| **Architecture** | Qwen3MoeForCausalLM (Mixture-of-Experts) |
| **Total Parameters** | 30B (3B active per token) |
| **Experts** | 128 per layer |
| **Quantization** | NVFP4 (4-bit NV floating point) |
| **KV Cache** | FP8 (8-bit float) |
| **Original Precision** | BF16 |
| **Quantized Size** | ~57 GB |
| **Quantization Tool** | NVIDIA ModelOpt 0.41.0 |
| **Calibration** | 512 samples (synthetic) |
| **Hardware** | NVIDIA DGX Spark GB10 (Blackwell) |

## Quantization Details

- **Method:** Post-training quantization via `nvidia-modelopt` with `NVFP4_DEFAULT_CFG`
- **Weights:** 4-bit NV floating point, group size 16
- **Activations:** 4-bit NV floating point, group size 16
- **KV Cache:** FP8 quantized for reduced memory during inference
- **Excluded layers:** `lm_head` and all MoE router/gate layers (48 total) — these remain in original precision to preserve routing quality
- **Export method:** HuggingFace `save_pretrained` with manual `quantization_config` injection (ModelOpt 0.41.0 native export does not yet support `Qwen3MoeExperts`)

## Usage

### With vLLM (Recommended)

```bash
vllm serve kleinpanic93/Qwen3-Coder-30B-A3B-Instruct-NVFP4 \
  --quantization modelopt \
  --trust-remote-code \
  --max-model-len 32768
```

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "kleinpanic93/Qwen3-Coder-30B-A3B-Instruct-NVFP4",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "kleinpanic93/Qwen3-Coder-30B-A3B-Instruct-NVFP4"
)
```

## Hardware Requirements

- **Minimum VRAM:** ~57 GB (unified memory or dedicated)
- **Tested on:** NVIDIA DGX Spark (GB10, 128 GB unified memory)
- **Recommended:** NVIDIA Blackwell GPUs (GB10, GB200, B200)

## Provenance

```json
{
  "source_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct",
  "quantization": "NVFP4",
  "tool": "nvidia-modelopt 0.41.0",
  "export_method": "save_pretrained_manual",
  "calib_size": 512,
  "calib_dataset": "synthetic-random",
  "hardware": "NVIDIA GB10 (Blackwell)",
  "elapsed_sec": 472
}
```

## Limitations

- This quantization uses **synthetic calibration data** (random tokens) because the container runs in offline mode. Production-grade quantization with real calibration data (e.g., C4, RedPajama) may yield slightly better quality.
- The export uses `save_pretrained` fallback rather than ModelOpt's native HF checkpoint exporter, since `Qwen3MoeExperts` is not yet in ModelOpt 0.41.0's export allowlist. The quantization math is identical — only the serialization path differs.
- MoE gate/router layers are preserved in original precision by design.

## License

This model inherits the [Apache 2.0 license](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE) from the base Qwen3-Coder-30B-A3B-Instruct model.

## Acknowledgments

- [Qwen Team](https://huggingface.co/Qwen) for the base model
- [NVIDIA](https://github.com/NVIDIA/TensorRT-Model-Optimizer) for ModelOpt quantization toolkit