[diffusion] feat: support loading Comfy NVFP4-AWQ text encoders (#36046)

docs/cookbook/diffusion/MiniMax/MiniMax-H3.mdx

@@ -1060,6 +1060,19 @@ W4A4 Qwen3-VL files use the same overlay, for example:
 This checkpoint keeps its unmarked embedding and vision tower in their source
 precision; no explicit component quantization option is needed.
 
+The official Comfy NVFP4-AWQ encoder uses the same flagless overlay:
+
+```bash Overlay
+--component-paths.text_encoder \
+  Comfy-Org/MiniMax-H3/text_encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
+```
+
+SGLang auto-detects its row-wise INT8 embedding, NVFP4 language linears, and
+AWQ input pre-scales. The weights stay compressed at rest; each active linear
+is dequantized for a BF16/FP16 matrix multiplication, so this path primarily
+reduces resident memory rather than encoder latency. Do not add a component or
+transformer quantization option.
+
 The same component option accepts a self-describing Quanto qint8 file without
 an additional quantization flag:
 

docs/docs/sglang-diffusion/quantization.mdx

@@ -53,12 +53,12 @@ repo contains multiple candidate checkpoints, pass
 `--transformer-weights-path` explicitly.
 
 MiniMax-H3 is a verified example for Comfy safetensors with per-layer metadata,
-including `pruned_fp8_scaled` and serialized ConvRot INT8. Other Comfy FP8
-exports are also auto-detected: the presence of an input scale selects static
-activation scaling, while its absence selects dynamic scaling. Pass one selected
-FL2VA or Ref2VA DiT file by local path, `owner/repo/path/file.safetensors`, or
-direct Hugging Face file URL; do not combine it with `--quantization`. Its GGUF
-usage is documented in
+including `pruned_fp8_scaled`, serialized ConvRot formats, and the official
+NVFP4-AWQ Qwen3-VL encoder. Other Comfy FP8 exports are also auto-detected: the
+presence of an input scale selects static activation scaling, while its absence
+selects dynamic scaling. Pass one selected DiT or component file by local path,
+`owner/repo/path/file.safetensors`, or direct Hugging Face file URL; do not
+combine it with an explicit quantization option. Its GGUF usage is documented in
 the [MiniMax-H3 cookbook](/cookbook/diffusion/MiniMax/MiniMax-H3#pre-quantized-gguf-transformer).
 
 ## Quantized Component Repositories
@@ -212,6 +212,14 @@ backend.
       <td><code>comfy-kitchen</code></td>
       <td>Auto-detected; omit <code>--quantization</code>. Each layer dispatches to its serialized W4A4 or INT8 ConvRot kernel. CUDA requires SM75+; TP must preserve each format's quantization and ConvRot group boundaries. Offload is supported and FSDP is not.</td>
     </tr>
+    <tr>
+      <td><code>comfy-nvfp4-full-precision</code></td>
+      <td>Safetensors with serialized <code>nvfp4</code> and optional row-wise <code>int8_tensorwise</code> layer metadata</td>
+      <td><code>--component-paths.text_encoder</code></td>
+      <td>MiniMax-H3 native Qwen3-VL encoder</td>
+      <td>None</td>
+      <td>Auto-detected; omit explicit quantization. Preserves packed storage, high-nibble-first weights, swizzled block scales, and AWQ input pre-scales. Each active NVFP4 matrix is dequantized for BF16/FP16 compute, so this is a memory path rather than a native FP4 speed path.</td>
+    </tr>
     <tr>
       <td><code>quanto-int8</code></td>
       <td>One native encoder safetensors file with an embedded Quanto quantization map</td>

python/sglang/multimodal_gen/runtime/layers/quantization/comfy_nvfp4.py

@@ -0,0 +1,257 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Portable full-precision execution for Comfy NVFP4 checkpoints."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from sglang.multimodal_gen.runtime.layers.linear import (
+    LinearBase,
+    UnquantizedLinearMethod,
+)
+from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
+    QuantizeMethodBase,
+)
+from sglang.multimodal_gen.runtime.layers.quantization.modelopt_quant import (
+    ModelOptFp4Config,
+    ModelOptFp4LinearMethod,
+    _swizzled_nvfp4_scales_to_linear,
+)
+from sglang.multimodal_gen.runtime.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding,
+)
+from sglang.multimodal_gen.runtime.utils.weight_attrs import set_weight_attrs
+from sglang.srt.layers.quantization.dequantization import dequantize_nvfp4
+
+
+def _register_parameter(
+    layer: nn.Module,
+    name: str,
+    data: torch.Tensor,
+    weight_attrs: dict[str, Any],
+    parallel_dims: dict[str, int] | None = None,
+) -> None:
+    parameter = nn.Parameter(data, requires_grad=False)
+    if parallel_dims is not None:
+        set_weight_attrs(parameter, parallel_dims)
+    set_weight_attrs(parameter, weight_attrs)
+    layer.register_parameter(name, parameter)
+
+
+class ComfyRowwiseInt8EmbeddingMethod(QuantizeMethodBase):
+    """Gather and dequantize only selected rows of an INT8 embedding."""
+
+    def create_weights(
+        self,
+        layer: nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs: Any,
+    ) -> None:
+        del input_size, output_size
+        self.output_dtype = params_dtype
+        output_size_per_partition = sum(output_partition_sizes)
+        _register_parameter(
+            layer,
+            "weight",
+            torch.empty(
+                output_size_per_partition,
+                input_size_per_partition,
+                dtype=torch.int8,
+            ),
+            extra_weight_attrs,
+            {"input_dim": 1, "output_dim": 0},
+        )
+        _register_parameter(
+            layer,
+            "weight_scale",
+            torch.empty(output_size_per_partition, 1, dtype=torch.float32),
+            extra_weight_attrs,
+            {"output_dim": 0},
+        )
+
+    def apply(
+        self,
+        layer: nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        raise NotImplementedError("Comfy INT8 embedding weights support lookup only")
+
+    def embedding(self, layer: nn.Module, input_: torch.Tensor) -> torch.Tensor:
+        weight = F.embedding(input_, layer.weight).to(self.output_dtype)
+        scale = F.embedding(input_, layer.weight_scale).to(self.output_dtype)
+        return weight * scale
+
+
+class ComfyFullPrecisionNvfp4LinearMethod(ModelOptFp4LinearMethod):
+    """Keep NVFP4 storage and dequantize one active Linear for its matmul."""
+
+    def __init__(
+        self,
+        quant_config: ComfyNvfp4Config,
+        *,
+        has_pre_quant_scale: bool,
+    ) -> None:
+        self.quant_config = quant_config
+        self.has_pre_quant_scale = has_pre_quant_scale
+
+    def create_weights(
+        self,
+        layer: nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs: Any,
+    ) -> None:
+        if len(output_partition_sizes) != 1:
+            raise ValueError(
+                "Comfy full_precision_matrix_mult does not support fused linears"
+            )
+        super().create_weights(
+            layer,
+            input_size_per_partition,
+            output_partition_sizes,
+            input_size,
+            output_size,
+            params_dtype,
+            **extra_weight_attrs,
+        )
+        # Comfy uses runtime activations directly for this weight-only path.
+        layer.register_parameter("input_scale", None)
+        if not self.has_pre_quant_scale:
+            return
+        _register_parameter(
+            layer,
+            "pre_quant_scale",
+            torch.empty(input_size_per_partition, dtype=params_dtype),
+            extra_weight_attrs,
+            {"input_dim": 0},
+        )
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        # The portable path consumes the serialized representation directly.
+        # ModelOpt's inherited hook instead prepares a Blackwell-only kernel.
+        return
+
+    def apply(
+        self,
+        layer: nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if self.has_pre_quant_scale:
+            x = x * layer.pre_quant_scale
+        weight_scale = _swizzled_nvfp4_scales_to_linear(layer.weight_scale)
+        weight = dequantize_nvfp4(
+            layer.weight,
+            weight_scale,
+            layer.weight_scale_2,
+            out_dtype=x.dtype,
+            high_nibble_first=True,
+        )
+        return F.linear(x, weight, bias)
+
+
+class ComfyNvfp4Config(ModelOptFp4Config):
+    """Dispatch full-precision Comfy NVFP4 linears and their INT8 embedding."""
+
+    checkpoint_uses_comfy_quantization = True
+
+    def __init__(self, layer_markers: dict[str, dict[str, Any]]) -> None:
+        super().__init__(
+            is_checkpoint_nvfp4_serialized=True,
+            group_size=16,
+            exclude_modules=[],
+            checkpoint_uses_comfy_quantization=True,
+        )
+        self.layer_markers = layer_markers
+        self.selected: list[str] = []
+        for prefix, marker in layer_markers.items():
+            marker_format = marker.get("format")
+            if marker_format == "int8_tensorwise" and marker.get("_is_rowwise"):
+                continue
+            if marker_format != "nvfp4":
+                raise ValueError(
+                    f"Unsupported Comfy NVFP4 companion for {prefix!r}: "
+                    f"{marker_format!r}"
+                )
+            if marker.get("full_precision_matrix_mult") is not True:
+                raise ValueError(
+                    f"Comfy NVFP4 layer {prefix!r} must request "
+                    "full_precision_matrix_mult"
+                )
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "comfy_nvfp4"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16, torch.float16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 0
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> ComfyNvfp4Config:
+        raise ValueError(
+            "comfy_nvfp4 is inferred from per-layer checkpoint metadata; "
+            "it is not an online quantization method"
+        )
+
+    def get_quant_method(
+        self, layer: nn.Module, prefix: str
+    ) -> QuantizeMethodBase | None:
+        marker = self.layer_markers.get(prefix)
+        if isinstance(layer, VocabParallelEmbedding):
+            if marker is None:
+                return None
+            if marker.get("format") != "int8_tensorwise" or not marker.get(
+                "_is_rowwise"
+            ):
+                raise ValueError(
+                    f"Unsupported quantized embedding marker for {prefix!r}: {marker}"
+                )
+            self.selected.append(prefix)
+            return ComfyRowwiseInt8EmbeddingMethod()
+        if not isinstance(layer, LinearBase):
+            return None
+        if marker is None:
+            return UnquantizedLinearMethod()
+        if marker.get("format") != "nvfp4":
+            raise ValueError(f"Unsupported quantized linear marker for {prefix!r}")
+        self.selected.append(prefix)
+        return ComfyFullPrecisionNvfp4LinearMethod(
+            self,
+            has_pre_quant_scale=bool(marker.get("_has_pre_quant_scale")),
+        )
+
+    def quantizes_embedding(self, prefix: str) -> bool:
+        marker = self.layer_markers.get(prefix)
+        return bool(
+            marker is not None
+            and marker.get("format") == "int8_tensorwise"
+            and marker.get("_is_rowwise")
+        )
+
+
+__all__ = [
+    "ComfyFullPrecisionNvfp4LinearMethod",
+    "ComfyNvfp4Config",
+    "ComfyRowwiseInt8EmbeddingMethod",
+]

python/sglang/multimodal_gen/runtime/loader/component_loaders/text_encoder_loader.py

@@ -31,6 +31,9 @@ from sglang.multimodal_gen.runtime.layers.linear import (
     UnquantizedLinearMethod,
 )
 from sglang.multimodal_gen.runtime.layers.quantization.comfy_fp8 import ComfyFp8Config
+from sglang.multimodal_gen.runtime.layers.quantization.comfy_nvfp4 import (
+    ComfyNvfp4Config,
+)
 from sglang.multimodal_gen.runtime.layers.quantization.configs.base_config import (
     QuantizationConfig,
 )
@@ -414,7 +417,13 @@ def _require_quantized_encoder_layers(
         )
     if isinstance(
         quant_config,
-        (ComfyFp8Config, KitchenInt8Config, KitchenW4A4Config, KitchenW4A8Config),
+        (
+            ComfyFp8Config,
+            ComfyNvfp4Config,
+            KitchenInt8Config,
+            KitchenW4A4Config,
+            KitchenW4A8Config,
+        ),
     ):
         expected = set(quant_config.layer_markers)
         selected = set(quant_config.selected)
@@ -932,7 +941,7 @@ class TextEncoderLoader(ComponentLoader):
 
             if quant_config is not None and not isinstance(quant_config, GGUFConfig):
                 postprocess_device: torch.device | None = local_torch_device
-                if isinstance(quant_config, QuantoInt8Config) or (
+                if isinstance(quant_config, (ComfyNvfp4Config, QuantoInt8Config)) or (
                     isinstance(quant_config, KitchenInt8Config)
                     and quant_config.is_checkpoint_int8_serialized
                 ):

python/sglang/multimodal_gen/runtime/utils/quantization_utils.py

@@ -13,6 +13,9 @@ from sglang.multimodal_gen.runtime.layers.quantization import (
     get_quantization_config,
 )
 from sglang.multimodal_gen.runtime.layers.quantization.comfy_fp8 import ComfyFp8Config
+from sglang.multimodal_gen.runtime.layers.quantization.comfy_nvfp4 import (
+    ComfyNvfp4Config,
+)
 from sglang.multimodal_gen.runtime.layers.quantization.configs.kitchen_int8_config import (
     KitchenInt8Config,
 )
@@ -84,6 +87,7 @@ def inspect_comfy_quant_markers(
                 if key.endswith(".weight") and tensor_slice.get_dtype() in (
                     "F8_E4M3",
                     "I8",
+                    "U8",
                 ):
                     marked_dtype_weight_prefixes.add(key.removesuffix(".weight"))
                 if not key.endswith(".comfy_quant"):
@@ -139,11 +143,14 @@ def inspect_comfy_quant_markers(
                 f"{prefix}.weight_s_rel",
                 f"{prefix}.weight_s_channel",
             }
+        if marker_format == "nvfp4":
+            required.add(f"{prefix}.weight_scale_2")
         if marker_format not in (
             "float8_e4m3fn",
             "int8_tensorwise",
             "asym_w4a8_int8",
             "convrot_w4a4",
+            "nvfp4",
         ):
             continue
         missing = required - checkpoint_meta.keys()
@@ -240,6 +247,44 @@ def inspect_comfy_quant_markers(
                     f"and convrot_groupsize={convrot_group_size}"
                 )
             continue
+        if marker_format == "nvfp4":
+            weight_dtype, weight_shape = checkpoint_meta[f"{prefix}.weight"]
+            scale_dtype, scale_shape = checkpoint_meta[f"{prefix}.weight_scale"]
+            scale_2_dtype, scale_2_shape = checkpoint_meta[f"{prefix}.weight_scale_2"]
+            if weight_dtype != "U8" or scale_dtype != "F8_E4M3":
+                raise ValueError(
+                    f"Comfy NVFP4 layer {prefix!r} needs U8 packed weights and "
+                    f"FP8 block scales, got {weight_dtype} and {scale_dtype}"
+                )
+            if scale_2_dtype != "F32" or scale_2_shape not in ((), (1,)):
+                raise ValueError(
+                    f"Comfy NVFP4 layer {prefix!r} needs a scalar F32 "
+                    f"weight_scale_2, got {scale_2_dtype}{scale_2_shape}"
+                )
+            if len(weight_shape) != 2:
+                raise ValueError(
+                    f"Comfy NVFP4 layer {prefix!r} needs a 2D packed weight, "
+                    f"got {weight_shape}"
+                )
+            logical_input_size = weight_shape[1] * 2
+            expected_scale_shape = (weight_shape[0], logical_input_size // 16)
+            if logical_input_size % 16 or scale_shape != expected_scale_shape:
+                raise ValueError(
+                    f"Comfy NVFP4 layer {prefix!r} has incompatible weight/scale "
+                    f"shapes: {weight_shape} and {scale_shape}"
+                )
+            pre_quant_scale_key = f"{prefix}.pre_quant_scale"
+            marker["_has_pre_quant_scale"] = pre_quant_scale_key in checkpoint_meta
+            if marker["_has_pre_quant_scale"]:
+                pre_scale_dtype, pre_scale_shape = checkpoint_meta[pre_quant_scale_key]
+                if pre_scale_dtype not in ("BF16", "F16", "F32") or (
+                    pre_scale_shape != (logical_input_size,)
+                ):
+                    raise ValueError(
+                        f"Comfy NVFP4 layer {prefix!r} has an incompatible "
+                        f"pre_quant_scale: {pre_scale_dtype}{pre_scale_shape}"
+                    )
+            continue
         if marker_format != "int8_tensorwise":
             continue
         weight_dtype, weight_shape = checkpoint_meta[f"{prefix}.weight"]
@@ -262,6 +307,7 @@ def inspect_comfy_quant_markers(
                 f"Comfy INT8 layer {prefix!r} has incompatible weight/scale "
                 f"shapes: {weight_shape} and {scale_shape}"
             )
+        marker["_is_rowwise"] = True
 
     mapped_markers: dict[str, dict[str, Any]] = {}
     for prefix, marker in raw_markers.items():
@@ -292,6 +338,8 @@ def resolve_comfy_checkpoint_quantization(
         return KitchenW4A4Config(layer_markers)
     if formats == ["float8_e4m3fn"]:
         return ComfyFp8Config(layer_markers)
+    if formats in (["nvfp4"], ["int8_tensorwise", "nvfp4"]):
+        return ComfyNvfp4Config(layer_markers)
     if formats == ["mxfp8"]:
         return MXFP8Config(
             is_checkpoint_fp8_serialized=True,

python/sglang/multimodal_gen/test/unit/test_text_encoder_loader.py

@@ -10,6 +10,11 @@ from safetensors.torch import save_file
 from torch import nn
 
 from sglang.multimodal_gen.runtime.layers.linear import LinearBase
+from sglang.multimodal_gen.runtime.layers.quantization.comfy_nvfp4 import (
+    ComfyFullPrecisionNvfp4LinearMethod,
+    ComfyNvfp4Config,
+    ComfyRowwiseInt8EmbeddingMethod,
+)
 from sglang.multimodal_gen.runtime.layers.quantization.configs.kitchen_int8_config import (
     KitchenInt8Config,
 )
@@ -526,6 +531,107 @@ class TestTextEncoderQuantization(unittest.TestCase):
             model_config.quant_config.layer_markers,
         )
 
+    def test_nvfp4_awq_weight_file_maps_embedding_and_linear_markers(self):
+        self.get_quant_config.return_value = None
+        layers = {
+            "model.embed_tokens": {"format": "int8_tensorwise"},
+            "model.layers.0.self_attn.o_proj": {
+                "format": "nvfp4",
+                "full_precision_matrix_mult": True,
+            },
+        }
+        with tempfile.NamedTemporaryFile(suffix=".safetensors") as checkpoint:
+            save_file(
+                {
+                    "model.embed_tokens.weight": torch.ones((4, 64), dtype=torch.int8),
+                    "model.embed_tokens.weight_scale": torch.ones(4, 1),
+                    "model.layers.0.self_attn.o_proj.weight": torch.full(
+                        (128, 32), 0x21, dtype=torch.uint8
+                    ),
+                    "model.layers.0.self_attn.o_proj.weight_scale": torch.ones(
+                        (128, 4), dtype=torch.float8_e4m3fn
+                    ),
+                    "model.layers.0.self_attn.o_proj.weight_scale_2": torch.tensor(0.5),
+                    "model.layers.0.self_attn.o_proj.pre_quant_scale": torch.ones(
+                        64, dtype=torch.bfloat16
+                    ),
+                },
+                checkpoint.name,
+                metadata={"_quantization_metadata": json.dumps({"layers": layers})},
+            )
+            model_config = SimpleNamespace(quant_config=None)
+            with mock.patch(
+                "sglang.multimodal_gen.runtime.loader.component_loaders."
+                "text_encoder_loader.get_quant_config_from_safetensors_metadata",
+                return_value=None,
+            ):
+                _configure_encoder_quantization(
+                    model_config,
+                    MiniMaxH3Qwen3VLEncoder,
+                    {},
+                    "/model/text_encoder",
+                    checkpoint.name,
+                    "text_encoder",
+                )
+
+        self.assertIsInstance(model_config.quant_config, ComfyNvfp4Config)
+        self.assertTrue(
+            model_config.quant_config.quantizes_embedding(
+                "model.language_model.embed_tokens"
+            )
+        )
+        marker = model_config.quant_config.layer_markers[
+            "model.language_model.layers.0.self_attn.o_proj"
+        ]
+        self.assertTrue(marker["_has_pre_quant_scale"])
+
+    def test_nvfp4_awq_portable_linear_and_rowwise_embedding(self):
+        config = ComfyNvfp4Config(
+            {
+                "proj": {
+                    "format": "nvfp4",
+                    "full_precision_matrix_mult": True,
+                    "_has_pre_quant_scale": True,
+                }
+            }
+        )
+        method = ComfyFullPrecisionNvfp4LinearMethod(config, has_pre_quant_scale=True)
+        layer = nn.Module()
+        layer.weight = nn.Parameter(
+            torch.full((128, 32), 0x21, dtype=torch.uint8), requires_grad=False
+        )
+        layer.weight_scale = nn.Parameter(
+            torch.ones((128, 4), dtype=torch.float8_e4m3fn), requires_grad=False
+        )
+        layer.weight_scale_2 = nn.Parameter(torch.tensor(0.5), requires_grad=False)
+        layer.pre_quant_scale = nn.Parameter(
+            torch.full((64,), 2.0), requires_grad=False
+        )
+        inputs = torch.zeros(1, 64)
+        inputs[:, 0::2] = 1
+
+        output = method.apply(layer, inputs)
+
+        torch.testing.assert_close(output, torch.full((1, 128), 32.0))
+
+        embedding_method = ComfyRowwiseInt8EmbeddingMethod()
+        embedding = nn.Module()
+        embedding_method.create_weights(
+            embedding,
+            input_size_per_partition=2,
+            output_partition_sizes=[3],
+            input_size=2,
+            output_size=3,
+            params_dtype=torch.bfloat16,
+        )
+        embedding.weight.data.copy_(torch.tensor([[1, 2], [3, 4], [5, 6]]))
+        embedding.weight_scale.data.copy_(torch.tensor([[0.5], [1.0], [2.0]]))
+        rows = embedding_method.embedding(embedding, torch.tensor([2, 0]))
+        torch.testing.assert_close(
+            rows,
+            torch.tensor([[10.0, 12.0], [0.5, 1.0]], dtype=torch.bfloat16),
+        )
+
     def test_gguf_maps_h3_names_and_drops_unused_language_layers(self):
         self.get_quant_config.return_value = None
 

python/sglang/srt/layers/quantization/dequantization.py

@@ -74,9 +74,11 @@ def dequantize_nvfp4(
     w_s: torch.Tensor,
     w_s2: Optional[torch.Tensor],
     out_dtype: torch.dtype = torch.bfloat16,
+    high_nibble_first: bool = False,
 ) -> torch.Tensor:
     """NVFP4 -> ``out_dtype``. ``w_q``: uint8 [..., out, in/2] packed e2m1
-    (low nibble = even idx). ``w_s``: fp8 e4m3 [..., out, in/16] per-block.
+    (low nibble = even idx unless ``high_nibble_first``). ``w_s``: fp8 e4m3
+    [..., out, in/16] per-block.
     ``w_s2``: optional fp32 per-tensor scalar that multiplies the per-block
     scale (ModelOpt / AMD Quark NVFP4)."""
     device = w_q.device
@@ -85,10 +87,11 @@ def dequantize_nvfp4(
 
     low = (w_q & 0xF).to(torch.int64)
     high = (w_q >> 4).to(torch.int64)
+    first, second = (high, low) if high_nibble_first else (low, high)
     lut = _FP4_E2M1_LUT.to(device=device, dtype=torch.float32)
     deq = torch.empty(*batch, out_dim, in_dim, dtype=torch.float32, device=device)
-    deq[..., 0::2] = lut[low]
-    deq[..., 1::2] = lut[high]
+    deq[..., 0::2] = lut[first]
+    deq[..., 1::2] = lut[second]
 
     scale = w_s.to(torch.float32)
     if w_s2 is not None: