# DeepSeek V4 Pro NVFP4 via NVIDIA ModelOpt

## What this does
Quantizes DeepSeek V4 Pro (FP8 weights) to full NVFP4 format using NVIDIA's official ModelOpt pipeline.
Target output: ~600GB (vs 840GB from custom Path A converter).

## Prerequisites
- B200 node (8× B200, 2.7TB RAM) — NVFP4 requires Blackwell GPUs
- modelopt 0.45.0+ from git
- transformers 5.8.0.dev0 (for DeepSeekV4 support)
- kernels package (for FP8 dequantization during calibration)

## Critical Patch
modelopt has a bug with DeepSeekV4Experts — the `iter_weights_for_calibration()` method
doesn't handle ModuleList quantizers (plural `gate_up_proj_weight_quantizers`).
Apply the patch before running:

```bash
cp patches/quant_module_patched.py <venv-path>/lib/python3.10/site-packages/modelopt/torch/quantization/nn/modules/quant_module.py
```

## Do NOT use these flags
- `--low_memory_mode`: causes meta device error with V4
- `--calib_size`: wrong arg name (use `--calib`)

## Run
```bash
bash scripts/run_modelopt_nvfp4.sh
```

## Output
`/root/nvidia-meeting/modelopt-repo/examples/llm_ptq/saved_models_DeepSeek-V4-Pro-FP8_nvfp4_kv_fp8_cast`

## Notes
- Use FP8 source (`DeepSeek-V4-Pro-FP8`), NOT mixed-precision BF16 (`DeepSeek-V4-Pro`)
- V4's mixed precision causes "wonky shit" — FP8 is clean
- Calibration takes hours with CPU offload (`--use_seq_device_map`)
- Expected calibration time: several hours for 256 samples