diff --git a/__init__.py b/__init__.py
index ce3b755..ac38af5 100644
--- a/__init__.py
+++ b/__init__.py
@@ -34,6 +34,12 @@ try:
 except Exception as _e:  # noqa
     print(f"[BFSNodes] MiniMax-H3 direct decode node not loaded: {_e!r}")
     MMDD_NODE_CLASS_MAPPINGS, MMDD_NODE_DISPLAY_NAME_MAPPINGS = {}, {}
+try:
+    from .save_h3_latent import NODE_CLASS_MAPPINGS as SVH3_NODE_CLASS_MAPPINGS
+    from .save_h3_latent import NODE_DISPLAY_NAME_MAPPINGS as SVH3_NODE_DISPLAY_NAME_MAPPINGS
+except Exception as _e:  # noqa
+    print(f"[BFSNodes] Save H3 Latent node not loaded: {_e!r}")
+    SVH3_NODE_CLASS_MAPPINGS, SVH3_NODE_DISPLAY_NAME_MAPPINGS = {}, {}
 try:
     from .ltx_identity_multiangle import NODE_CLASS_MAPPINGS as MA_NODE_CLASS_MAPPINGS
     from .ltx_identity_multiangle import NODE_DISPLAY_NAME_MAPPINGS as MA_NODE_DISPLAY_NAME_MAPPINGS
@@ -80,6 +86,7 @@ NODE_CLASS_MAPPINGS = {
     **MMVAE_NODE_CLASS_MAPPINGS,
     **SHARP_NODE_CLASS_MAPPINGS,
     **MMDD_NODE_CLASS_MAPPINGS,
+    **SVH3_NODE_CLASS_MAPPINGS,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -100,4 +107,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     **MMVAE_NODE_DISPLAY_NAME_MAPPINGS,
     **SHARP_NODE_DISPLAY_NAME_MAPPINGS,
     **MMDD_NODE_DISPLAY_NAME_MAPPINGS,
+    **SVH3_NODE_DISPLAY_NAME_MAPPINGS,
 }
diff --git a/pyproject.toml b/pyproject.toml
index 8c838cb..10671d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "bfsnodes"
 description = "Custom ComfyUI nodes including LTXV Edit Anything, Head-Swap/Anime2Real Bernini conditioning, the AMV Guide Builder, LTX Identity Transfer (IPT2V reference + source-phase RoPE), and MiniMax-H3 single-frame VAE loaders with tile control."
-version = "1.26.2"
+version = "1.27.0"
 license = {file = "LICENSE"}
 dependencies = [
     "torch",
diff --git a/save_h3_latent.py b/save_h3_latent.py
new file mode 100644
index 0000000..6a5cc10
--- /dev/null
+++ b/save_h3_latent.py
@@ -0,0 +1,55 @@
+"""Salva um latente do MiniMax-H3 em disco.
+
+O `SaveLatent` nativo quebra com o latente do H3: ele chega como NestedTensor (video + audio)
+e o node chama `.contiguous()` direto. Aqui os componentes sao desempacotados e salvos
+separadamente, para o latente poder ser inspecionado ou decodificado fora do grafo.
+"""
+
+import os
+
+import folder_paths
+import torch
+from safetensors.torch import save_file
+
+
+class SaveH3Latent:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "samples": ("LATENT",),
+                "filename_prefix": ("STRING", {"default": "h3_latent"}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    CATEGORY = "BFS/debug"
+
+    def save(self, samples, filename_prefix):
+        z = samples["samples"]
+        tensores = {}
+        if getattr(z, "is_nested", False):
+            for i, parte in enumerate(z.unbind()):
+                t = parte if parte.dim() == 5 else parte.unsqueeze(0)
+                tensores[f"component_{i}"] = t.contiguous().cpu()
+        else:
+            t = z if z.dim() == 5 else z.unsqueeze(0)
+            tensores["component_0"] = t.contiguous().cpu()
+
+        pasta = folder_paths.get_output_directory()
+        n = 0
+        while os.path.exists(os.path.join(pasta, f"{filename_prefix}_{n:05d}.safetensors")):
+            n += 1
+        caminho = os.path.join(pasta, f"{filename_prefix}_{n:05d}.safetensors")
+        meta = {k: str(tuple(v.shape)) for k, v in tensores.items()}
+        save_file(tensores, caminho, metadata={"format": "pt", **meta})
+        print(f"[BFSNodes] latente salvo em {caminho}")
+        for k, v in tensores.items():
+            print(f"[BFSNodes]   {k}: {tuple(v.shape)} {v.dtype}")
+        return {}
+
+
+NODE_CLASS_MAPPINGS = {"SaveH3Latent": SaveH3Latent}
+NODE_DISPLAY_NAME_MAPPINGS = {"SaveH3Latent": "Save H3 Latent / nested-safe (BFS)"}