diff --git a/test/prototype/quantization/quantize_/float8/test_float8_semisparse_tensor.py b/test/prototype/quantization/quantize_/float8/test_float8_semisparse_tensor.py
new file mode 100644
index 0000000000..b71006f380
--- /dev/null
+++ b/test/prototype/quantization/quantize_/float8/test_float8_semisparse_tensor.py
@@ -0,0 +1,151 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+from torch.testing._internal import common_utils
+
+from torchao.prototype.quantization.quantize_.workflows.float8.float8_semisparse_tensor import (
+    Float8SemiSparseTensor,
+)
+from torchao.testing.utils import TorchAOIntegrationTestCase
+
+
+@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+@common_utils.instantiate_parametrized_tests
+class TestFloat8SemiSparseTensor(TorchAOIntegrationTestCase):
+    def setUp(self):
+        super().setUp()
+        torch.manual_seed(42)
+
+        # Use 512x512 for 2:4 compatibility (multiples of 32)
+        self.shape = (512, 512)
+        self.dtype = torch.bfloat16
+        self.block_size = [1, 512]
+        self.weight_fp = torch.randn(*self.shape, dtype=self.dtype, device="cuda")
+
+    def test_creation_and_shape(self):
+        """Test tensor creation and shape preservation"""
+        tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+
+        self.assertEqual(tensor.shape, self.shape)
+        self.assertEqual(tensor.original_shape, self.shape)
+        self.assertEqual(tensor.scale.shape[0], self.shape[0])
+
+    def test_sparsity_pattern(self):
+        """Test 2:4 sparsity pattern is maintained"""
+        tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+        dequantized = tensor.dequantize()
+
+        # Check 2:4 pattern
+        reshaped = dequantized.reshape(-1, 4)
+        zeros_per_group = (reshaped == 0).sum(dim=1)
+        valid_groups = (zeros_per_group == 2).sum().item()
+        total_groups = zeros_per_group.numel()
+
+        self.assertGreaterEqual(valid_groups / total_groups, 0.99)
+
+    def test_dequantization_accuracy(self):
+        """Test dequantization error is reasonable"""
+        tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+        dequantized = tensor.dequantize()
+
+        # Apply same pruning to original for fair comparison
+        w_sparse = self.weight_fp.detach().clone()
+        pruning_inds = w_sparse.abs().view(-1, 4).argsort(dim=1)[:, :2]
+        w_sparse.view(-1, 4).scatter_(1, pruning_inds, value=0)
+
+        # Norm-based metrics for numerical stability
+        error = (dequantized - w_sparse).abs()
+        max_error = error.max()
+        mean_error = error.mean()
+
+        # Relative error using non-zero elements
+        non_zero_mask = w_sparse != 0
+        if non_zero_mask.any():
+            rel_error = (error[non_zero_mask] / w_sparse[non_zero_mask].abs()).mean()
+            self.assertLess(rel_error.item(), 0.1)
+
+        self.assertLess(max_error.item(), 1.0)
+        self.assertLess(mean_error.item(), 0.1)
+
+    def test_invalid_dimensions(self):
+        """Test dimension validation"""
+        # Not multiple of 32
+        invalid_weight = torch.randn(100, 100, dtype=self.dtype, device="cuda")
+
+        with self.assertRaises(ValueError):
+            Float8SemiSparseTensor.from_hp(invalid_weight, [1, 100])
+
+    def test_device(self):
+        """Test if device handler work"""
+        # CPU tensor should be rejected
+        cpu_weight = torch.randn(*self.shape, dtype=self.dtype, device="cpu")
+        with self.assertRaises(ValueError):
+            Float8SemiSparseTensor.from_hp(cpu_weight, self.block_size)
+
+        # CUDA tensor components should all be on CUDA
+        tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+        self.assertEqual(tensor.qdata.device, tensor.qdata_compressed.device)
+        self.assertEqual(tensor.qdata.device, tensor.scale.device)
+        self.assertTrue(tensor.qdata.is_cuda)
+
+    def test_w8a8_dynamic_activation(self):
+        """Test W8A8-FP-CSR with dynamic activation quantization"""
+        weight_tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+
+        batch_size = 32
+        in_features = self.shape[1]
+        activation = torch.randn(
+            batch_size, in_features, dtype=self.dtype, device="cuda"
+        )
+        output = torch.nn.functional.linear(activation, weight_tensor)
+
+        expected_shape = (batch_size, self.shape[0])
+        self.assertEqual(output.shape, expected_shape)
+        self.assertEqual(output.dtype, self.dtype)
+        self.assertFalse(output.isnan().any())
+        self.assertFalse(output.isinf().any())
+
+    def test_linear_with_bias(self):
+        """Test linear operation with bias"""
+        weight_tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+        activation = torch.randn(32, self.shape[1], dtype=self.dtype, device="cuda")
+        bias = torch.randn(self.shape[0], dtype=self.dtype, device="cuda")
+
+        output = torch.nn.functional.linear(activation, weight_tensor, bias)
+
+        self.assertEqual(output.shape, (32, self.shape[0]))
+        self.assertEqual(output.dtype, self.dtype)
+        self.assertFalse(output.isnan().any())
+        self.assertFalse(output.isinf().any())
+
+    def test_batched_input(self):
+        """Test 3D batched input"""
+        weight_tensor = Float8SemiSparseTensor.from_hp(self.weight_fp, self.block_size)
+        batch_dims = (4, 8)
+        activation = torch.randn(
+            *batch_dims, self.shape[1], dtype=self.dtype, device="cuda"
+        )
+
+        output = torch.nn.functional.linear(activation, weight_tensor)
+
+        expected_shape = (*batch_dims, self.shape[0])
+        self.assertEqual(output.shape, expected_shape)
+        self.assertEqual(output.dtype, self.dtype)
+        self.assertFalse(output.isnan().any())
+
+    def test_zero_weight_validation(self):
+        """Test scale validation with zero weights"""
+        zero_weight = torch.zeros(*self.shape, dtype=self.dtype, device="cuda")
+
+        with self.assertRaises(ValueError):
+            Float8SemiSparseTensor.from_hp(zero_weight, self.block_size)
+
+
+if __name__ == "__main__":
+    common_utils.run_tests()
diff --git a/torchao/prototype/quantization/quantize_/workflows/__init__.py b/torchao/prototype/quantization/quantize_/workflows/__init__.py
new file mode 100644
index 0000000000..e0b4edf34a
--- /dev/null
+++ b/torchao/prototype/quantization/quantize_/workflows/__init__.py
@@ -0,0 +1,7 @@
+from .float8.float8_semisparse_tensor import (
+    Float8SemiSparseTensor,
+)
+
+__all__ = [
+    "Float8SemiSparseTensor",
+]
diff --git a/torchao/prototype/quantization/quantize_/workflows/float8/float8_semisparse_tensor.py b/torchao/prototype/quantization/quantize_/workflows/float8/float8_semisparse_tensor.py
new file mode 100644
index 0000000000..1d5d988bca
--- /dev/null
+++ b/torchao/prototype/quantization/quantize_/workflows/float8/float8_semisparse_tensor.py
@@ -0,0 +1,232 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional
+
+import torch
+from torch.sparse import SparseSemiStructuredTensorCUSPARSELT, to_sparse_semi_structured
+
+from torchao.quantization.quant_primitives import (
+    _choose_qparams_affine_floatx,
+)
+from torchao.utils import TorchAOBaseTensor
+
+__all__ = ["Float8SemiSparseTensor"]
+
+aten = torch.ops.aten
+
+
+class Float8SemiSparseTensor(TorchAOBaseTensor):
+    """
+    W8A8-FP-CSR: float8 quantized tensor with 2:4 semi-structured sparsity layout
+
+    Tensor Attributes:
+        qdata: float8 data in dense format
+        qdata_compressed: SparseSemiStructuredTensor (compressed format for matmul)
+        scale: scale factors for dequantization
+
+    Non-Tensor Attributes:
+        block_size: block size for quantization granularity
+        original_shape: original uncompressed shape
+        float8_dtype: float8 dtype variant
+    """
+
+    tensor_data_names = ["qdata", "qdata_compressed", "scale"]
+    tensor_attribute_names = ["block_size", "original_shape"]
+    optional_tensor_attribute_names = ["dtype"]
+
+    def __new__(
+        cls: type,
+        qdata: torch.Tensor,
+        qdata_compressed: torch.Tensor,
+        scale: torch.Tensor,
+        block_size: list[int],
+        original_shape: tuple[int, ...],
+        dtype=None,
+        float8_dtype: torch.dtype = torch.float8_e4m3fn,
+    ):
+        kwargs = {
+            "device": qdata.device,
+            "dtype": dtype or scale.dtype,
+            "requires_grad": False,
+        }
+        return torch.Tensor._make_wrapper_subclass(cls, original_shape, **kwargs)
+
+    def __init__(
+        self,
+        qdata: torch.Tensor,
+        qdata_compressed: torch.Tensor,
+        scale: torch.Tensor,
+        block_size: list[int],
+        original_shape: tuple[int, ...],
+        dtype=None,
+        float8_dtype: torch.dtype = torch.float8_e4m3fn,
+    ):
+        super().__init__()
+        self.qdata = qdata  # dense fp8 for dequantization
+        self.qdata_compressed = qdata_compressed  # compressed for matmul
+        self.scale = scale
+        self.block_size = block_size
+        self.original_shape = original_shape
+        self.float8_dtype = float8_dtype
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__name__}("
+            f"qdata_shape={self.qdata.shape}, {self.scale=}, "
+            f"{self.block_size=}, {self.shape=}, {self.device=}, {self.dtype=})"
+        )
+
+    @property
+    def qdata_fp8(self):
+        """For test compatibility"""
+        return self.qdata
+
+    @classmethod
+    def from_hp(
+        cls,
+        w: torch.Tensor,
+        block_size: list[int],
+        float8_dtype: torch.dtype = torch.float8_e4m3fn,
+    ):
+        if w.dim() != 2 or len(block_size) != 2:
+            raise ValueError("Expected 2D tensor and block_size length 2")
+
+        if not w.is_cuda:
+            raise ValueError("Semi-sparse layout requires CUDA tensors")
+
+        # Verify dimensions are compatible with 2:4 compression
+        rows, cols = w.shape
+        if rows % 32 != 0 or cols % 32 != 0:
+            raise ValueError(
+                "Tensor dimensions must be multiples of 32 for CUDA sparse compression"
+            )
+
+        # Validate block_size
+        if not all(bs > 0 for bs in block_size):
+            raise ValueError(f"block_size must be positive, got {block_size}")
+
+        if rows % block_size[0] != 0 or cols % block_size[1] != 0:
+            raise ValueError(
+                f"Dimensions {w.shape} must be divisible by block_size {block_size}"
+            )
+
+        # Apply 2:4 sparsity pruning
+        with torch.no_grad():
+            w_sparse = w.clone()
+
+        pruning_inds = w_sparse.abs().view(-1, 4).argsort(dim=1)[:, :2]
+        w_sparse.view(-1, 4).scatter_(1, pruning_inds, value=0)
+
+        # Check for all-zero (sparsity=1) tensor
+        if w_sparse.abs().max() == 0:
+            raise ValueError("Input tensor is all zeros after pruning")
+
+        scale = _choose_qparams_affine_floatx(w_sparse, ebits=4, mbits=3)
+
+        if not torch.isfinite(scale).all():
+            raise ValueError("Scale contains NaN or inf values")
+        if not (scale > 0).all():
+            raise ValueError(f"Scale contains non-positive values: min={scale.min()}")
+
+        scale_expanded = scale.unsqueeze(1)
+        scaled_data = w_sparse / scale_expanded
+        scaled_data = scaled_data.clamp(-448.0, 448.0)
+        fp8_data = scaled_data.to(float8_dtype).contiguous()
+
+        if not torch.isfinite(fp8_data.to(torch.float32)).all():
+            raise ValueError("fp8_data contains NaN/Inf after quantization")
+
+        # Store fp8 data in both dense and compressed formats
+        fp8_data_fp16 = fp8_data.to(torch.float16)
+
+        fp8_compressed = to_sparse_semi_structured(fp8_data_fp16)
+
+        return cls(
+            fp8_data,  # dense for dequantization
+            fp8_compressed,  # compressed for matmul
+            scale,
+            block_size,
+            original_shape=w.shape,
+            dtype=w.dtype,
+            float8_dtype=float8_dtype,
+        )
+
+    def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor:
+        if output_dtype is None:
+            output_dtype = self.dtype
+
+        # Use dense fp8 data
+        qdata_fp = self.qdata.to(output_dtype)
+        scale_expanded = self.scale.view(-1, 1).to(output_dtype)
+        return qdata_fp * scale_expanded
+
+
+implements = Float8SemiSparseTensor.implements
+implements_torch_function = Float8SemiSparseTensor.implements_torch_function
+
+
+@implements(aten.linear.default)
+@implements_torch_function(torch.nn.functional.linear)
+def _(func, types, args, kwargs):
+    activation_tensor, weight_tensor, bias = (
+        args[0],
+        args[1],
+        args[2] if len(args) > 2 else None,
+    )
+
+    assert isinstance(weight_tensor, Float8SemiSparseTensor)
+    assert activation_tensor.dim() == 2, "Only 2D input supported"
+    assert activation_tensor.shape[-1] == weight_tensor.original_shape[1]
+
+    x_scales = _choose_qparams_affine_floatx(activation_tensor, ebits=4, mbits=3)
+    w_scales = weight_tensor.scale
+
+    # Global normalizer to prevent overflow
+    global_scale = (x_scales.max() * w_scales.max()).sqrt().clamp(min=0.01)
+    x_scales_adj = (x_scales.unsqueeze(1) / global_scale).to(torch.float32)
+    scaled_x = (activation_tensor.to(torch.float32) / x_scales_adj).clamp(-448.0, 448.0)
+    x_vals_fp8 = scaled_x.to(torch.float8_e4m3fn)
+
+    # MatMul
+    x_padded = SparseSemiStructuredTensorCUSPARSELT._pad_dense_input(
+        x_vals_fp8.to(torch.float16)
+    )
+    y_fp16 = torch.matmul(x_padded, weight_tensor.qdata_compressed.t())
+    y = y_fp16[: activation_tensor.shape[0], :].to(torch.float32)
+
+    # Restore scale
+    w_scales_fp32 = w_scales.to(torch.float32)
+    y = y * (x_scales_adj * w_scales_fp32.unsqueeze(0) * global_scale)
+    y = y.to(activation_tensor.dtype).contiguous()
+
+    if bias is not None:
+        y += bias
+    return y
+
+
+@implements(aten.slice.Tensor)
+def _(func, types, args, kwargs):
+    """Slice operation - not supported for compressed sparse format"""
+    # TODO: Build this tensor utility operation
+    raise NotImplementedError(
+        "Slicing not supported for Float8SemiSparseTensor. "
+        "Decompress first using dequantize() if needed."
+    )
+
+
+@implements(aten.select.int)
+def _(func, types, args, kwargs):
+    """Select operation - not supported for compressed sparse format"""
+    # TODO: Build this tensor utility operation
+    raise NotImplementedError(
+        "Select not supported for Float8SemiSparseTensor. "
+        "Decompress first using dequantize() if needed."
+    )
+
+
+Float8SemiSparseTensor.__module__ = "torchao.quantization"
+torch.serialization.add_safe_globals([Float8SemiSparseTensor])