diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index 4054f1b7f5..04c15bb2c3 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -1084,6 +1084,15 @@ class FastModel(FastBaseModel):
                 ";"
                 "os.environ['TRITON_F32_DEFAULT'] = 'ieee'"
             )
+        elif "nemotron_h" in model_types_all:
+            # NemotronH (hybrid Mamba-2 + Transformer) uses same Mamba kernels as Falcon-H1
+            # Mamba kernels need float32 Triton precision
+            os.environ["UNSLOTH_FORCE_CUSTOM_DTYPE"] = (
+                "float16;torch.float32;torch.float16;"
+                "if name.endswith(('q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj', 'head')): module.to(torch.float16)"
+                ";"
+                "os.environ['TRITON_F32_DEFAULT'] = 'ieee'"
+            )
         elif "gpt_oss" in model_types_all:
             os.environ["UNSLOTH_DISABLE_STATIC_GENERATION"] = "1"
             if not load_in_4bit:
@@ -1281,7 +1290,21 @@ class FastModel(FastBaseModel):
         is_vlm = any(x.endswith("ForConditionalGeneration") for x in architectures)
         is_vlm = is_vlm or hasattr(model_config, "vision_config")
         if auto_model is None:
-            auto_model = AutoModelForVision2Seq if is_vlm else AutoModelForCausalLM
+            if is_vlm:
+                # Check if the model's auto_map supports the VLM auto class.
+                # Some VL models (e.g. Nemotron-VL) only register AutoModelForCausalLM
+                # in their auto_map, not AutoModelForImageTextToText/AutoModelForVision2Seq.
+                _auto_map = getattr(model_config, "auto_map", {}) or {}
+                _vlm_class_name = AutoModelForVision2Seq.__name__
+                if (
+                    "AutoModelForCausalLM" in _auto_map
+                    and _vlm_class_name not in _auto_map
+                ):
+                    auto_model = AutoModelForCausalLM
+                else:
+                    auto_model = AutoModelForVision2Seq
+            else:
+                auto_model = AutoModelForCausalLM
 
         load_in_4bit_kwargs = load_in_4bit
         load_in_8bit_kwargs = load_in_8bit
diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py
index 56cd615a49..24e72e1535 100644
--- a/unsloth/models/vision.py
+++ b/unsloth/models/vision.py
@@ -654,18 +654,25 @@ class FastBaseModel:
             raise RuntimeError(
                 "Unsloth: Can only load in 4bit or 8bit or 16bit, not a combination!"
             )
+        _skip_modules = SKIP_QUANTIZATION_MODULES.copy()
+        # Nemotron-H uses 'mixer' (not 'mamba') for Mamba layers.
+        # Mamba fused kernels pass out_proj.weight directly to F.linear,
+        # which fails with quantized Params4bit. Skip out_proj from quantization.
+        if any(mt == "nemotron_h" for mt in (model_types or [])):
+            _skip_modules.append("out_proj")
+
         if load_in_4bit:
             bnb_config = BitsAndBytesConfig(
                 load_in_4bit = True,
                 bnb_4bit_use_double_quant = True,
                 bnb_4bit_quant_type = "nf4",
                 bnb_4bit_compute_dtype = bnb_compute_dtype,
-                llm_int8_skip_modules = SKIP_QUANTIZATION_MODULES.copy(),
+                llm_int8_skip_modules = _skip_modules,
             )
         elif load_in_8bit:
             bnb_config = BitsAndBytesConfig(
                 load_in_8bit = True,
-                llm_int8_skip_modules = SKIP_QUANTIZATION_MODULES.copy(),
+                llm_int8_skip_modules = _skip_modules,
             )
         elif load_in_16bit:
             bnb_config = None