diff --git a/examples/distributed_inference/README.md b/examples/distributed_inference/README.md
index c164e9581f..4c88570b6f 100644
--- a/examples/distributed_inference/README.md
+++ b/examples/distributed_inference/README.md
@@ -11,4 +11,6 @@ See the examples started with `data_parallel` for more details.
 
 2. Tensor parallel distributed inference
 
-In development.
+Here we use torch.distributed as an example, but compilation with tensor parallelism is agnostic to the implementation framework as long as the module is properly sharded.
+
+torchrun --nproc_per_node=2 tensor_parallel_llama2.py
diff --git a/examples/distributed_inference/llama3_model.py b/examples/distributed_inference/llama3_model.py
new file mode 100644
index 0000000000..9fa59b5c49
--- /dev/null
+++ b/examples/distributed_inference/llama3_model.py
@@ -0,0 +1,538 @@
+# Taken and modified pytorch lightening
+# https://lightning.ai/lightning-ai/studios/tensor-parallelism-supercharging-large-model-training-with-pytorch-lightning
+
+
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.distributed._tensor import Replicate, Shard
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor.parallel import (
+    ColwiseParallel,
+    PrepareModuleInput,
+    RowwiseParallel,
+    SequenceParallel,
+    parallelize_module,
+)
+
+
+@dataclass
+class ModelArgs:
+    dim: int = 4096
+    n_layers: int = 32
+    n_heads: int = 32
+    n_kv_heads: Optional[int] = None
+    vocab_size: int = -1  # defined later by tokenizer
+    multiple_of: int = 256  # make SwiGLU hidden layer size multiple of large power of 2
+    ffn_dim_multiplier: Optional[float] = None
+    norm_eps: float = 1e-5
+    rope_theta: float = 10000
+
+    max_batch_size: int = 32
+    max_seq_len: int = 2048
+    # If `True`, then each transformer block init uses its layer ID, and if
+    # `False`, each uses the total number of transformer blocks
+    depth_init: bool = True
+    device: str = "cuda"
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0) -> torch.Tensor:
+    """Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
+
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        end (int): End index for precomputing frequencies.
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials.
+
+    """
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)
+    freqs = torch.outer(t, freqs).float()
+    return torch.polar(torch.ones_like(freqs), freqs)  # complex64
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+    """Reshape frequency tensor for broadcasting it with another tensor.
+
+    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+    for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+    The input freqs_cis tensor is assumed to be of shape (max_seqlen, dim),
+    and the first seqlen elements will be sliced, but dim must match x.
+
+    Args:
+        freqs_cis (torch.Tensor): Frequency tensor to be reshaped.
+        x (torch.Tensor): Target tensor for broadcasting compatibility.
+
+    Returns:
+        torch.Tensor: Reshaped frequency tensor.
+
+    """
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    seqlen = x.shape[1]
+    freqs_cis = freqs_cis[0:seqlen]
+    assert freqs_cis.shape == (seqlen, x.shape[-1])
+    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+    return freqs_cis.view(*shape)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Apply rotary embeddings to input tensors using the given frequency tensor.
+
+    This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided
+    frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor
+    is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are
+    returned as real tensors.
+
+    Args:
+        xq (torch.Tensor): Query tensor to apply rotary embeddings.
+        xk (torch.Tensor): Key tensor to apply rotary embeddings.
+        freqs_cis (torch.Tensor): Precomputed frequency tensor for complex exponentials.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+
+    """
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+class RMSNorm(nn.Module):
+    """Initialize the RMSNorm normalization layer.
+
+    Args:
+        dim (int): The dimension of the input tensor.
+        eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+    Attributes:
+        eps (float): A small value added to the denominator for numerical stability.
+        weight (nn.Parameter): Learnable scaling parameter.
+
+    """
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x: torch.Tensor):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x: torch.Tensor):
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+    def reset_parameters(self):
+        torch.nn.init.ones_(self.weight)  # type: ignore
+
+
+class Attention(nn.Module):
+    """Multi-head attention module.
+
+    Args:
+        model_args (ModelArgs): Model configuration arguments.
+
+    Attributes:
+        n_kv_heads (int): Number of key and value heads.
+        n_heads (int): Number of query heads.
+        n_rep (int): Number of repetitions for local heads.
+        head_dim (int): Dimension size of each attention head.
+        wq (Linear): Linear transformation for queries.
+        wk (Linear): Linear transformation for keys.
+        wv (Linear): Linear transformation for values.
+        wo (Linear): Linear transformation for output.
+
+    """
+
+    def __init__(self, model_args: ModelArgs):
+        super().__init__()
+        self.n_heads = model_args.n_heads
+        self.n_kv_heads = (
+            model_args.n_heads
+            if model_args.n_kv_heads is None
+            else model_args.n_kv_heads
+        )
+        self.n_rep = self.n_heads // self.n_kv_heads
+        self.head_dim = model_args.dim // model_args.n_heads
+
+        self.wq = nn.Linear(
+            model_args.dim, model_args.n_heads * self.head_dim, bias=False
+        )
+        self.wk = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(model_args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(
+            model_args.n_heads * self.head_dim, model_args.dim, bias=False
+        )
+
+    def init_weights(self, init_std: float) -> None:
+        for linear in (self.wq, self.wk, self.wv):
+            nn.init.trunc_normal_(linear.weight, mean=0.0, std=0.02)
+        nn.init.trunc_normal_(self.wo.weight, mean=0.0, std=init_std)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> Any:
+        """Forward pass of the attention module.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor.
+
+        Returns:
+            torch.Tensor: Output tensor after attention.
+
+        """
+        bs, seqlen, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+
+        xq = xq.view(bs, seqlen, self.n_heads, self.head_dim)
+        xk = xk.view(bs, seqlen, self.n_kv_heads, self.head_dim)
+        xv = xv.view(bs, seqlen, self.n_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        keys = repeat_kv(xk, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
+        values = repeat_kv(xv, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
+
+        xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
+        xk = keys.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
+        xv = values.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
+
+        # we use casual mask for training
+        output = F.scaled_dot_product_attention(xq, xk, xv, is_causal=True)
+        output = output.transpose(
+            1, 2
+        ).contiguous()  # (bs, seqlen, n_local_heads, head_dim)
+        output = output.view(bs, seqlen, -1)
+        return self.wo(output)
+
+
+class FeedForward(nn.Module):
+    """FeedForward module.
+
+    Args:
+        dim (int): Input dimension.
+        hidden_dim (int): Hidden dimension of the feedforward layer.
+        multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+        ffn_dim_multiplier (Optional[float]): Custom multiplier for hidden dimension. Defaults to None.
+
+    Attributes:
+        w1 (Linear): Linear transformation for the first layer.
+        w2 (Linear): Linear transformation for the second layer.
+        w3 (Linear): Linear transformation for the third layer.
+
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+    ):
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x) -> Any:
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+    def init_weights(self, init_std: float) -> None:
+        nn.init.trunc_normal_(self.w1.weight, mean=0.0, std=0.02)
+        for linear in (self.w2, self.w3):
+            nn.init.trunc_normal_(linear.weight, mean=0.0, std=init_std)
+
+
+class TransformerBlock(nn.Module):
+    """TransformerBlock Module.
+
+    Args:
+        layer_id (int): Identifier for the layer.
+        model_args (ModelArgs): Model configuration arguments.
+
+    Attributes:
+        n_heads (int): Number of attention heads.
+        dim (int): Dimension size of the model.
+        head_dim (int): Dimension size of each attention head.
+        attention (Attention): Attention module.
+        feed_forward (FeedForward): FeedForward module.
+        layer_id (int): Identifier for the layer.
+        attention_norm (RMSNorm): Layer normalization for attention output.
+        ffn_norm (RMSNorm): Layer normalization for feedforward output.
+
+    """
+
+    def __init__(self, layer_id: int, model_args: ModelArgs):
+        super().__init__()
+        self.n_heads = model_args.n_heads
+        self.dim = model_args.dim
+        self.attention = Attention(model_args)
+        self.feed_forward = FeedForward(
+            dim=model_args.dim,
+            hidden_dim=4 * model_args.dim,
+            multiple_of=model_args.multiple_of,
+            ffn_dim_multiplier=model_args.ffn_dim_multiplier,
+        )
+        self.layer_id = layer_id
+        self.num_layers = model_args.n_layers
+
+        self.attention_norm = RMSNorm(dim=model_args.dim, eps=model_args.norm_eps)
+        self.ffn_norm = RMSNorm(dim=model_args.dim, eps=model_args.norm_eps)
+
+        if model_args.depth_init:
+            self.weight_init_std = 0.02 / (2 * (self.layer_id + 1)) ** 0.5
+        else:
+            self.weight_init_std = 0.02 / (2 * self.num_layers) ** 0.5
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ):
+        """Perform a forward pass through the TransformerBlock.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+
+        Returns:
+            torch.Tensor: Output tensor after applying attention and feedforward layers.
+
+        """
+        h = x + self.attention(self.attention_norm(x), freqs_cis)
+        return h + self.feed_forward(self.ffn_norm(h))
+
+    def init_weights(self):
+        for norm in (self.attention_norm, self.ffn_norm):
+            norm.reset_parameters()
+        self.attention.init_weights(self.weight_init_std)
+        self.feed_forward.init_weights(self.weight_init_std)
+
+
+class ParallelTransformer(nn.Module):
+    """Transformer Module.
+
+    Args:
+        model_args (ModelArgs): Model configuration arguments.
+
+    Attributes:
+        model_args (ModelArgs): Model configuration arguments.
+        vocab_size (int): Vocabulary size.
+        n_layers (int): Number of layers in the model.
+        tok_embeddings (ParallelEmbedding): Token embeddings.
+        layers (torch.nn.ModuleList): List of Transformer blocks.
+        norm (RMSNorm): Layer normalization for the model output.
+        output (ColumnParallelLinear): Linear layer for final output.
+        freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+
+    """
+
+    def __init__(self, model_args: ModelArgs, tp_mesh: DeviceMesh = None):
+        # Here we use distributed model initialization to avoid memory overflow
+        super().__init__()
+        self.model_args = model_args
+        self.vocab_size = model_args.vocab_size
+        self.n_layers = model_args.n_layers
+
+        self.tok_embeddings = nn.Embedding(model_args.vocab_size, model_args.dim)
+        self.tok_embeddings.to(model_args.device)
+        self.tok_embeddings = self.parallel_embeddings(self.tok_embeddings, tp_mesh)
+
+        # TODO persistent should be set to false, since this buffer can be recomputed.
+        # however, we set it to true for 2 reasons.  (1) due to pytorch/pytorch#123411,
+        # compile or pipeline-tracer will not correctly handle non-persistent buffers,
+        # so we need to fix that.  (2) if we initialize pipeline-parallel models from
+        # a seed checkpoint rather than calling init_weights, we need freqs_cis to be
+        # initialized by the checkpoint, or we need to add a separate initializer for
+        # just the non-persistent buffers that is called after loading checkpoints.
+        self.register_buffer(
+            "freqs_cis",
+            self._precompute_freqs_cis().to(model_args.device),
+            persistent=True,
+        )
+
+        self.layers = torch.nn.ModuleDict().to(model_args.device)
+        for layer_id in range(model_args.n_layers):
+            block = TransformerBlock(layer_id, model_args).to(model_args.device)
+            self.layers[str(layer_id)] = block
+            self.parallel_transformer_block(self.layers[str(layer_id)], tp_mesh)
+
+        self.norm = RMSNorm(dim=model_args.dim, eps=model_args.norm_eps).to(
+            model_args.device
+        )
+        self.norm = self.parallel_norm(self.norm, tp_mesh)
+        self.output = nn.Linear(model_args.dim, model_args.vocab_size, bias=False).to(
+            model_args.device
+        )
+        self.output = self.parallel_output(self.output, tp_mesh)
+        self.init_weights()
+
+    def parallel_transformer_block(self, transformer_block, tp_mesh):
+        if tp_mesh.size() <= 1:
+            return
+        plan = {
+            "attention": PrepareModuleInput(
+                input_layouts=(Shard(1), None),
+                desired_input_layouts=(Replicate(), None),
+            ),
+            "attention.wq": ColwiseParallel(),
+            "attention.wk": ColwiseParallel(),
+            "attention.wv": ColwiseParallel(),
+            "attention.wo": RowwiseParallel(output_layouts=Shard(1)),
+            "attention_norm": SequenceParallel(),
+            "feed_forward": PrepareModuleInput(
+                input_layouts=(Shard(1),),
+                desired_input_layouts=(Replicate(),),
+            ),
+            "feed_forward.w1": ColwiseParallel(),
+            "feed_forward.w2": RowwiseParallel(output_layouts=Shard(1)),
+            "feed_forward.w3": ColwiseParallel(),
+            "ffn_norm": SequenceParallel(),
+        }
+
+        # Adjust attention module to use the local number of heads
+        attn_layer = transformer_block.attention
+        attn_layer.n_heads = attn_layer.n_heads // tp_mesh.size()
+        attn_layer.n_kv_heads = attn_layer.n_kv_heads // tp_mesh.size()
+
+        # Apply the plan for the current transformer block
+        parallelize_module(transformer_block, tp_mesh, plan)
+
+    def parallel_embeddings(self, embedding, tp_mesh):
+        plan = {
+            "tok_embeddings": RowwiseParallel(
+                input_layouts=Replicate(),
+                output_layouts=Shard(1),
+            )
+        }
+        return parallelize_module(embedding, tp_mesh, plan)
+
+    def parallel_output(self, output, tp_mesh):
+        plan = {
+            "output": ColwiseParallel(
+                input_layouts=Shard(1),
+            ),
+        }
+        return parallelize_module(output, tp_mesh, plan)
+
+    def parallel_norm(self, norm, tp_mesh):
+        plan = {
+            "norm": SequenceParallel(),
+        }
+        return parallelize_module(norm, tp_mesh, plan)
+
+    def reset_parameters(self):
+        with torch.device(self.freqs_cis.device):
+            self.freqs_cis = self._precompute_freqs_cis()
+
+    def init_weights(self):
+        """[Note: On ``init_weights`` vs.
+
+        ``reset_parameters``]
+        Modules may define ``reset_parameters`` to initialize parameter values.
+        ``reset_parameters`` is meant to only initialize directly owned
+        parameters/buffers, not those of their child modules, and it can be
+        used to give the initial values for these tensors.
+        Separately, users may want custom initialization for their modules,
+        different from that in ``reset_parameters``. For this, we define
+        ``init_weights``. We only call it in the constructor of this
+        ``Transformer`` root module to avoid reinitializing tensors.
+
+        """
+        with torch.device(self.freqs_cis.device):
+            self.freqs_cis = self._precompute_freqs_cis()
+        nn.init.normal_(self.tok_embeddings.weight)
+        for layer in self.layers.values():
+            layer.init_weights()
+        self.norm.reset_parameters()
+        final_out_std = self.model_args.dim**-0.5
+        cutoff_factor = 3
+        nn.init.trunc_normal_(
+            self.output.weight,
+            mean=0.0,
+            std=final_out_std,
+            a=-cutoff_factor * final_out_std,
+            b=cutoff_factor * final_out_std,
+        )
+
+    def _precompute_freqs_cis(self) -> torch.Tensor:
+        return precompute_freqs_cis(
+            self.model_args.dim // self.model_args.n_heads,
+            # Need to compute until at least the max token limit for generation
+            # (use 2x max sequence length to be safe)
+            self.model_args.max_seq_len * 2,
+            self.model_args.rope_theta,
+        )
+
+    def forward(self, tokens: torch.Tensor):
+        """Perform a forward pass through the Transformer model.
+
+        Args:
+            tokens (torch.Tensor): Input token indices.
+
+        Returns:
+            torch.Tensor: Output logits after applying the Transformer model.
+
+        """
+        # passthrough for nonexistent layers, allows easy configuration of pipeline parallel stages
+        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens
+
+        for layer in self.layers.values():
+            h = layer(h, self.freqs_cis)
+
+        h = self.norm(h) if self.norm else h
+        return self.output(h).float() if self.output else h
+
+    @classmethod
+    def from_model_args(cls, model_args: ModelArgs) -> "Transformer":
+        """Initialize a Transformer model from a ModelArgs object.
+
+        Args:
+            model_args (ModelArgs): Model configuration arguments.
+
+        Returns:
+            Transformer: Transformer model.
+
+        """
+        return cls(model_args)
diff --git a/examples/distributed_inference/tensor_parallel_llama3.py b/examples/distributed_inference/tensor_parallel_llama3.py
new file mode 100644
index 0000000000..fc03a64386
--- /dev/null
+++ b/examples/distributed_inference/tensor_parallel_llama3.py
@@ -0,0 +1,72 @@
+# Taken and modified pytorch lightening
+# https://lightning.ai/lightning-ai/studios/tensor-parallelism-supercharging-large-model-training-with-pytorch-lightning
+import logging
+import os
+import time
+
+import torch
+import torch_tensorrt
+from llama3_model import ModelArgs, ParallelTransformer
+from torch.distributed._composable.fsdp import MixedPrecisionPolicy
+from torch.distributed._composable.fsdp.fully_shard import fully_shard
+from torch.distributed._tensor import Replicate, Shard
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    checkpoint_wrapper,
+)
+from torch.distributed.device_mesh import DeviceMesh, init_device_mesh
+
+_rank = int(os.environ["RANK"])
+_world_size = int(os.environ["WORLD_SIZE"])
+tp_size = 2
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+fh = logging.FileHandler(f"./tensor_parallel_log_{_rank}.log", mode="w")
+fh.setLevel(logging.INFO)
+logger.addHandler(fh)
+
+tp_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
+
+model_args = ModelArgs(
+    vocab_size=32000,
+    dim=1024,
+    n_layers=4,
+    n_heads=8,
+    rope_theta=500000.0,
+    n_kv_heads=8,
+    device="cuda",
+)
+
+with torch.no_grad():
+    model = ParallelTransformer(model_args, tp_mesh)
+    torch.manual_seed(0)
+    inp = torch.randint(32000, (8, 256), device="cuda")
+    python_result = model(inp)
+    torch_tensorrt.runtime.set_multi_device_safe_mode(True)
+    model = torch.compile(
+        model,
+        fullgraph=True,
+        backend="torch_tensorrt",
+        options={
+            "truncate_long_and_double": True,
+            "enabled_precisions": {torch.float32, torch.float16},
+            "use_python_runtime": True,
+            "workspace_size": 1 << 33,
+            "debug": False,
+            "timing_cache_path": "/opt/file/cache/timing_cache_llama.bin",
+        },
+        dynamic=False,
+    )
+    for i in range(15):
+        # seeding with dp_rank to ensure identical inputs for TP groups
+        torch.manual_seed(i)
+        start = time.time()
+        output = model(inp)
+        end = time.time()
+        if i == 0:
+            logger.info(f"Compilation time is {end-start}")
+            assert (
+                python_result - output
+            ).std() < 0.01, "Compilation result is not correct."
+        elif _rank == 0:
+            logger.info(f"Inference time is {end-start}")
diff --git a/examples/distributed_inference/tensor_parallel_simple_example.py b/examples/distributed_inference/tensor_parallel_simple_example.py
new file mode 100755
index 0000000000..470487a751
--- /dev/null
+++ b/examples/distributed_inference/tensor_parallel_simple_example.py
@@ -0,0 +1,99 @@
+import os
+import sys
+import time
+
+import torch
+import torch.nn as nn
+import torch_tensorrt
+from torch.distributed._tensor import Shard
+from torch.distributed._tensor.device_mesh import init_device_mesh
+from torch.distributed.tensor.parallel import (
+    ColwiseParallel,
+    RowwiseParallel,
+    parallelize_module,
+)
+
+"""
+This example copies some code from https://github.com/pytorch/examples/blob/main/distributed/tensor_parallelism/tensor_parallel_example.py
+"""
+
+
+class ToyModel(nn.Module):
+    """MLP based model"""
+
+    def __init__(self):
+        super(ToyModel, self).__init__()
+        self.in_proj = nn.Linear(10, 3200)
+        self.relu = nn.ReLU()
+        self.out_proj = nn.Linear(3200, 1600)
+        self.in_proj2 = nn.Linear(1600, 500)
+        self.out_proj2 = nn.Linear(500, 100)
+
+    def forward(self, x):
+        x = self.out_proj(self.relu(self.in_proj(x)))
+        x = self.relu(x)
+        x = self.out_proj2(self.relu(self.in_proj2(x)))
+        return x
+
+
+# create a device mesh based on the given world_size.
+_world_size = int(os.environ["WORLD_SIZE"])
+
+device_mesh = init_device_mesh(device_type="cuda", mesh_shape=(_world_size,))
+_rank = device_mesh.get_rank()
+
+
+print(f"Starting PyTorch TP example on rank {_rank}.")
+assert (
+    _world_size % 2 == 0
+), f"TP examples require even number of GPUs, but got {_world_size} gpus"
+
+
+# # create model and move it to GPU - init"cuda"_mesh has already mapped GPU ids.
+tp_model = ToyModel().to("cuda")
+
+
+# Custom parallelization plan for the model
+tp_model = parallelize_module(
+    module=tp_model,
+    device_mesh=device_mesh,
+    parallelize_plan={
+        "in_proj": ColwiseParallel(input_layouts=Shard(0)),
+        "out_proj": RowwiseParallel(output_layouts=Shard(0)),
+        "in_proj2": ColwiseParallel(input_layouts=Shard(0)),
+        "out_proj2": RowwiseParallel(output_layouts=Shard(0)),
+    },
+)
+torch.manual_seed(0)
+inp = torch.rand(20, 10, device="cuda")
+python_result = tp_model(inp)
+
+
+backend = "torch_tensorrt"
+tp_model = torch.compile(
+    tp_model,
+    backend=backend,
+    options={
+        "truncate_long_and_double": True,
+        "enabled_precisions": {torch.float32, torch.float16},
+        "use_python_runtime": True,
+        "min_block_size": 1,
+    },
+    dynamic=False,
+)
+
+for i in range(10):
+    # For TP, input needs to be same across all TP ranks.
+    # Setting the random seed is to mimic the behavior of dataloader.
+    torch.manual_seed(i)
+    inp = torch.rand(20, 10, device="cuda")
+    start = time.time()
+    output = tp_model(inp)
+    end = time.time()
+    if i == 0:
+        print(f"Compilation time is {end-start}")
+        assert (
+            python_result - output
+        ).std() < 0.01, "Compilation result is not correct."
+    elif _rank == 0:
+        print(f"Inference time is {end-start}")