diff --git a/tools/validate_cute_qkv_block.py b/tools/validate_cute_qkv_block.py
new file mode 100644
index 0000000..84811c1
--- /dev/null
+++ b/tools/validate_cute_qkv_block.py
@@ -0,0 +1,157 @@
+"""Alternate baseline and CuTe-QKV execution inside one loaded H3 block."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+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.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("--output", type=Path, required=True)
+    parser.add_argument("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
+    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("--attention", default="sage2")
+    parser.add_argument(
+        "--feature", choices=("qkv_ring", "modulate_fusion", "swiglu_fusion"), default="qkv_ring",
+    )
+    parser.add_argument("--warmup", type=int, default=2)
+    parser.add_argument("--iterations", type=int, default=10)
+    parser.add_argument("--device", default="cuda")
+    return parser.parse_args()
+
+
+def sync() -> None:
+    torch.cuda.synchronize()
+
+
+def summarize(values: list[float]) -> dict[str, float]:
+    ordered = sorted(values)
+    middle = len(ordered) // 2
+    median = (
+        ordered[middle]
+        if len(ordered) % 2
+        else (ordered[middle - 1] + ordered[middle]) / 2
+    )
+    return {
+        "mean_s": sum(values) / len(values),
+        "p50_s": median,
+        "min_s": ordered[0],
+        "max_s": ordered[-1],
+    }
+
+
+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=args.attention,
+    ).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,
+    )
+    sigmas = beta_sigmas(args.steps, device=args.device)
+    sigma = sigmas[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,
+    )
+    adaln_values = tuple(value.detach() for value in adaln(timesteps))
+
+    def run(enabled: bool):
+        if args.feature == "modulate_fusion":
+            block.fused_nvfp4_modulation = enabled
+        elif args.feature == "swiglu_fusion":
+            block.mlp.fused_nvfp4_swiglu = enabled
+        else:
+            if enabled:
+                os.environ["H3_CUTE_QKV_RING"] = "1"
+            else:
+                os.environ.pop("H3_CUTE_QKV_RING", None)
+        return block(hidden, rotation, *adaln_values, segments)
+
+    with torch.inference_mode():
+        reference = run(False)
+        candidate = run(True)
+        sync()
+        delta = candidate.float() - reference.float()
+        for _ in range(args.warmup):
+            run(False)
+            run(True)
+        sync()
+        baseline_times = []
+        candidate_times = []
+        last_reference = reference
+        last_candidate = candidate
+        for _ in range(args.iterations):
+            sync()
+            started = time.perf_counter()
+            last_reference = run(False)
+            sync()
+            baseline_times.append(time.perf_counter() - started)
+
+            sync()
+            started = time.perf_counter()
+            last_candidate = run(True)
+            sync()
+            candidate_times.append(time.perf_counter() - started)
+
+    baseline = summarize(baseline_times)
+    candidate_timing = summarize(candidate_times)
+    report = {
+        "device": torch.cuda.get_device_name(),
+        "block_index": args.block_index,
+        "feature": args.feature,
+        "hidden_shape": list(hidden.shape),
+        "iterations": args.iterations,
+        "equal": torch.equal(reference, candidate),
+        "max_abs": delta.abs().max().item(),
+        "mean_abs": delta.abs().mean().item(),
+        "reference_checksum": last_reference.float().sum().item(),
+        "candidate_checksum": last_candidate.float().sum().item(),
+        "baseline": baseline,
+        "candidate": candidate_timing,
+        "p50_improvement_percent": (
+            1.0 - candidate_timing["p50_s"] / baseline["p50_s"]
+        ) * 100.0,
+    }
+    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 __name__ == "__main__":
+    main()