diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index 884c51e8ea..2b32e5e015 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -928,6 +928,30 @@ def aten_ops_slice(
     )
 
 
+@dynamo_tensorrt_converter(torch.ops.aten.chunk.default)
+@enforce_tensor_types(
+    {
+        0: (TRTTensor,),
+    }
+)
+def aten_ops_chunk(
+    ctx: ConversionContext,
+    target: Target,
+    args: Tuple[Argument, ...],
+    kwargs: Dict[str, Argument],
+    name: str,
+) -> Union[TRTTensor, Sequence[TRTTensor]]:
+    return impl.slice.chunk(
+        ctx,
+        target,
+        SourceIR.ATEN,
+        name,
+        args[0],
+        args[1],
+        args_bounds_check(args, 2, 0),
+    )
+
+
 def refit_validator(node: Node, settings: CompilationSettings = None) -> bool:
     # cumsum op is not refitable
     if settings and settings.make_refittable:
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/slice/ops.py b/py/torch_tensorrt/dynamo/conversion/impl/slice/ops.py
index 3274d78c2b..9b5c96b162 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/slice/ops.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/slice/ops.py
@@ -655,3 +655,58 @@ def nested(
     )
 
     return reshape_output
+
+
+def chunk(
+    ctx: ConversionContext,
+    target: Target,
+    source_ir: Optional[SourceIR],
+    name: str,
+    input: TRTTensor,
+    chunks: int,
+    dim: int,
+) -> TRTTensor:
+    if chunks <= 0:
+        raise RuntimeError(
+            f"chunk expects `chunks` to be greater than 0, got: {chunks}"
+        )
+
+    shape = input.shape
+    dim = get_positive_dim(dim, len(shape))
+
+    if dim >= len(shape):
+        raise RuntimeError(
+            f"chunk expects `dim` to be less than the length of input shape, got: {dim}"
+        )
+
+    dynamic_shape = has_dynamic_shape(input.shape)
+    if dynamic_shape > 0:
+        # Check whether slice target dim is dynamic shape dim
+        assert input.shape[dim] != -1, "Can't chunk on dynamic shape dimension!"
+
+    size_dim = shape[dim]
+    chunk_size = math.ceil(size_dim / chunks)
+    result = []
+    start = 0
+    end = min(start + chunk_size, size_dim)
+    cnt = 0
+
+    while start < end:
+        result.append(
+            slice_op(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_slice_{cnt}",
+                input,
+                dim,
+                start,
+                end,
+                1,
+            )
+        )
+        start = end
+        end = min(start + chunk_size, size_dim)
+        cnt += 1
+
+    return result