diff --git a/tools/validate_nvfp4_swiglu_producer.py b/tools/validate_nvfp4_swiglu_producer.py
new file mode 100644
index 0000000..7650115
--- /dev/null
+++ b/tools/validate_nvfp4_swiglu_producer.py
@@ -0,0 +1,135 @@
+"""Validate fused SwiGLU and native NVFP4 production on real H3 FC1 output."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import torch
+
+from h3_blackwell_runtime.adaln import H3CurveAdaLN
+from h3_blackwell_runtime.block import H3DiTBlock
+from h3_blackwell_runtime.checkpoint import H3Checkpoint
+from h3_blackwell_runtime.nvfp4_quant import (
+    nvfp4_activation_scale,
+    vortex_native_quantize_swiglu_nvfp4,
+)
+from h3_blackwell_runtime.packing import H3PromptPacker
+from h3_blackwell_runtime.rope import h3_rope_rotation
+from h3_blackwell_runtime.sampler import _audio_sigma, _model_sigma, beta_sigmas
+from h3_blackwell_runtime.t2v import random_av_latents
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--block-index", type=int, required=True)
+    parser.add_argument("--width", type=int, default=1344)
+    parser.add_argument("--height", type=int, default=768)
+    parser.add_argument("--frames", type=int, default=124)
+    parser.add_argument("--steps", type=int, default=12)
+    parser.add_argument("--sampler-step", type=int, default=1)
+    parser.add_argument("--seed", type=int, default=440420)
+    parser.add_argument("--text-tokens", type=int, default=100)
+    parser.add_argument("--warmup", type=int, default=3)
+    parser.add_argument("--iterations", type=int, default=10)
+    parser.add_argument("--device", default="cuda")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    torch.manual_seed(args.seed)
+    checkpoint = H3Checkpoint(args.model_path, device=args.device)
+    block = H3DiTBlock.from_checkpoint(
+        checkpoint, args.block_index, attention_backend="sage2",
+    ).eval()
+    adaln = H3CurveAdaLN.from_checkpoint(
+        checkpoint, f"blocks.{args.block_index}.adaln_proj",
+    ).eval()
+    packer = H3PromptPacker(checkpoint)
+    video, audio, _ = random_av_latents(
+        args.width, args.height, args.frames, args.seed, device=args.device,
+    )
+    sigma = beta_sigmas(args.steps, device=args.device)[args.sampler_step - 1]
+    native_audio = audio.to(torch.bfloat16) * (_audio_sigma(sigma) / sigma)
+    text = torch.randn(
+        1, args.text_tokens, 5376, device=args.device, dtype=torch.bfloat16,
+    )
+    hidden, timesteps, segments, positions, _, _ = packer(
+        text, video, native_audio, _model_sigma(sigma),
+    )
+    rotation = h3_rope_rotation(
+        positions.to(args.device),
+        checkpoint.tensor("rope.inv_freq", dtype=torch.float32),
+        hidden.dtype,
+    )
+    modulation = tuple(value.detach() for value in adaln(timesteps))
+    captured = []
+    block.fused_nvfp4_modulation = False
+    hook = block.mlp.fc1.register_forward_hook(
+        lambda _module, _inputs, output: captured.append(output.detach()),
+    )
+    with torch.inference_mode():
+        block(hidden, rotation, *modulation, segments)
+    hook.remove()
+    gate_up = captured[-1]
+
+    with torch.inference_mode():
+        gate, up = gate_up.chunk(2, dim=-1)
+        materialized = torch.nn.functional.silu(gate).mul_(up)
+        reference_scale = nvfp4_activation_scale(materialized).float()
+        import comfy_kitchen as ck
+        from comfy_kitchen.tensor import TensorCoreNVFP4Layout
+
+        reference_qdata, reference_sfa = ck.quantize_nvfp4(
+            materialized,
+            reference_scale,
+            pad_16x=TensorCoreNVFP4Layout.get_padded_shape(tuple(materialized.shape))
+            != tuple(materialized.shape),
+        )
+        actual_scale, actual_qdata, actual_sfa = vortex_native_quantize_swiglu_nvfp4(
+            gate_up,
+        )
+        torch.cuda.synchronize()
+        for _ in range(args.warmup):
+            vortex_native_quantize_swiglu_nvfp4(gate_up)
+        times = []
+        for _ in range(args.iterations):
+            torch.cuda.synchronize()
+            started = time.perf_counter()
+            vortex_native_quantize_swiglu_nvfp4(gate_up)
+            torch.cuda.synchronize()
+            times.append(time.perf_counter() - started)
+
+    report = {
+        "device": torch.cuda.get_device_name(),
+        "block_index": args.block_index,
+        "input_shape": list(gate_up.shape),
+        "output_shape": list(materialized.shape),
+        "scale_equal": torch.equal(actual_scale, reference_scale),
+        "scale_reference": reference_scale.item(),
+        "scale_actual": actual_scale.item(),
+        "qdata_differences": torch.count_nonzero(actual_qdata != reference_qdata).item(),
+        "sfa_differences": torch.count_nonzero(
+            actual_sfa.view(torch.uint8) != reference_sfa.view(torch.uint8)
+        ).item(),
+        "producer_p50_ms": sorted(times)[len(times) // 2] * 1000.0,
+    }
+    report["equal"] = (
+        report["scale_equal"]
+        and report["qdata_differences"] == 0
+        and report["sfa_differences"] == 0
+    )
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
+    print(json.dumps(report, indent=2), flush=True)
+    if not report["equal"]:
+        raise RuntimeError("fused SwiGLU producer is not byte-exact")
+
+
+if __name__ == "__main__":
+    main()