DeepSeek V4 Pro → NVFP4 Quantization

Full NVFP4 quantization of DeepSeek V4 Pro on a single B200 node (8× B200, 2.7TB RAM, 13TB NVMe). Target: ~600GB.

Cost: ~$161/run at $23/hr (7 hours each). Don't waste runs.

Pipeline

Step 1: Dequantize FP8 → BF16

python3 scripts/dequant_fp8_to_bf16.py /root/nvidia-meeting/DeepSeek-V4-Pro-FP8 /root/nvidia-meeting/DeepSeek-V4-Pro-BF16

The original V4 weights use mixed precision (FP8 attention + FP4/E2M1 experts with per-tensor scales). We dequantize everything to pure BF16 so modelopt can run calibration without hitting broken FP8 kernel paths on Blackwell (DeepGEMM unsupported, Triton finegrained FP8 matmul shape mismatches).

This is not a blind upcast — it applies the actual scale factors:

W_bf16 = dequantize_fp4_weight(W_int, S)  # per-tensor scale dequant, not .to(bfloat16)

Byte-exact verified — matmul diff is 0.000000 against the official inference path.

Step 2: Run NVFP4 Quantization

cd /root/nvidia-meeting/modelopt-repo/examples/llm_ptq
python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/quantize_nvfp4.py

Must run from the modelopt example directory (relative imports).

Pipeline steps:

1. Load BF16 model via modelopt's get_model() (handles memory limits properly for 3TB model)
2. Patch modelopt at runtime (load_calib_amax forces CPU, export_amax CPU fallback, graceful clamp)
3. Quantize + Calibrate (5-6 hours, 128 samples)
4. Snapshot amax to CPU — copies all quantizer state to CPU and saves to disk (~50MB)
5. Save model state — full state dict to disk (insurance against export crashes)
6. Export to HF safetensors

If the export crashes:

python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/quantize_nvfp4.py --export-only

To validate saved state without running anything:

python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/quantize_nvfp4.py --validate-only

Config: nvfp4, 128 calib samples, calib_seq=512, kv_fp8_cast, gpu_mem_pct=0.7

Calibration datasets: abisee/cnn_dailymail + nvidia/Nemotron-Post-Training-Dataset-v2 (gated — requires HF token).

Runtime: Model loading ~53 min. Calibration ~5.5 hours. Export ~30-60 min. Total 7-8 hours.

Run History (forward progression)

Run	Date	Commit	Calib	Result	Root Cause	Fix
1	May 7	shell wrapper (pre-repo)	256	❌ Crashed at batch probing	o_b_proj shape mismatch — finegrained_fp8 wraps MLA projections incorrectly with FP8 source	Use BF16 source (dequantized)
2	May 8-9	shell wrapper (pre-repo)	128	❌ Crashed at export (128/128 calib ✅)	get_activation_scaling_factor reads stale GPU amax → CUDA illegal memory access	Snapshot amax to CPU immediately after calibration
3	May 9 06:10	3907838	128	❌ Crashed at model loading	AutoModelForCausalLM.from_pretrained OOM during expert weight torch.cat (31.5GB alloc, 25.9GB free)	Use modelopt get_model() which handles max_memory properly
4	May 9 ~08:05	f9bbef8	128	🔄 Running	—	—

If Run 4 succeeds, current code is good. No further changes needed. If Run 4 fails, check the log, identify the crash point, add it to this table.

Key lesson from Run 2: The use_seq_device_map mode shuffles layers through GPU for calibration. Quantizer amax tensors sit in VRAM for 5+ hours while CUDA's allocator churns memory. By export time, the GPU tensor metadata is valid but the underlying memory has been recycled — reading it triggers cudaErrorIllegalAddress. The fix is to copy amax to CPU immediately after calibration, before any further GPU operations.

Key lesson from Run 3: Don't use AutoModelForCausalLM.from_pretrained directly for models with large expert weights. The weight conversion step does torch.cat on GPU which can OOM. Use modelopt's get_model() which sets max_memory per GPU before loading.

Do NOT repeat these mistakes:

• Don't use FP8 source model — kernel issues on Blackwell (Run 1)
• Don't use --low_memory_mode with V4 — meta device errors
• Don't use calib_size=256 — OOMs with 3TB BF16 on CPU offload
• Don't use AutoModelForCausalLM.from_pretrained directly — OOM during expert weight concat (Run 3)
• Don't assume GPU tensor integrity after 5+ hours of sequential calibration

Runtime Patches Applied by quantize_nvfp4.py

These are monkey-patches applied at runtime — no modelopt source files are modified.

1. load_calib_amax — After calibration writes _amax to GPU, immediately moves it to CPU. Prevents stale GPU tensors.
2. export_amax — If any _amax is still on GPU at export time, moves to CPU before reading. Safety net.
3. get_activation_scaling_factor — Moves amax to CPU, clamps bad values instead of hard assert. Prevents crash on garbage from GPU corruption.
4. snapshot_amax_to_cpu() — After calibration, walks all quantizers, copies _amax to CPU, saves to disk (~50MB). Insurance policy.
5. force_all_amax_to_cpu() — Moves _pre_quant_scale, _global_amax to CPU too. Nuclear option.

Bugs Found (V4 + modelopt 0.45.0.dev64)

1. QuantDeepseekV4Experts AttributeError — Already fixed in modelopt 0.45.0.dev64 (handles nn.ModuleList quantizers natively).
2. --low_memory_mode → meta device error. Don't use with V4.
3. Missing kernels package for FP8 ops. pip install -U kernels.
4. Shell script arg names — No longer relevant (in-process script).
5. Export crash — stale GPU tensors in export_amax(). After hours of calibration, quantizer _amax on GPU becomes unreadable. Fixed by patching export_amax to move _amax to CPU before reading.
6. Export crash — assert torch.all(activation_scaling_factor > 0). Amax values from stale GPU reads are garbage (zeros, negatives, NaN). Fixed by clamping instead of asserting, plus snapshotting valid amax to CPU before corruption can occur.
7. Model loading OOM during expert weight conversion. AutoModelForCausalLM.from_pretrained does torch.cat on GPU for expert gate_up_proj (31.5GB alloc), but only 25.9GB free with device_map="sequential". Fixed by using modelopt's get_model() which sets max_memory per GPU before loading.

Dependencies (pinned versions)

• nvidia-modelopt: 0.45.0.dev64+g579fc6c31 (installed from git, not PyPI)
• transformers: 5.8.0.dev0 (from git, required for DeepSeekV4 support)
• kernels: latest (pip install -U kernels — needed for finegrained FP8 ops)
• Python: 3.10

The patches in quantize_nvfp4.py are for modelopt 0.45.0.dev64 specifically. Later versions may include fixes natively — check before applying.

Key Notes

• V4 is NOT BF16 — it ships as mixed-precision FP8/FP4. You MUST dequantize to BF16 first (Step 1).
• --low_memory_mode causes meta device errors with V4 — don't use.
• modelopt has no explicit V4 support — relies on auto-detection of fused experts.
• The calibration state save (v4_nvfp4_calibrated_state.pt) is ~1.5TB. It lives on NVMe, not in git.
• The amax snapshot (v4_nvfp4_amax_snapshots.pt) is ~50MB. Small, critical, cheap insurance.
• Only divergence from modelopt example path: get_model() instead of AutoModelForCausalLM.from_pretrained (avoids OOM). Everything else uses the same hf_ptq functions.