diff --git a/examples/llm_ptq/convert_kimi_k2_to_trtllm.py b/examples/llm_ptq/convert_kimi_k2_to_trtllm.py
new file mode 100644
index 000000000..deb696291
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+++ b/examples/llm_ptq/convert_kimi_k2_to_trtllm.py
@@ -0,0 +1,371 @@
+#!/usr/bin/env python3
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Convert Kimi-K2-Thinking compressed-tensors checkpoint to TRT-LLM GPTQ format.
+
+The source checkpoint uses:
+- int32 packing: 8 int4 values per int32
+- group_size: 32
+- symmetric quantization
+
+TRT-LLM GPTQ format uses:
+- int32 packing: 8 int4 values per int32 (same)
+- Requires .qweight, .scales, .qzeros tensors
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+import safetensors
+import safetensors.torch
+import torch
+from tqdm import tqdm
+
+
+def unpack_int32_to_int4(weight_packed: torch.Tensor) -> torch.Tensor:
+    """
+    Unpack int32 tensor containing 8 int4 values into int8 tensor.
+
+    Args:
+        weight_packed: Shape (N, K/8) dtype int32
+
+    Returns:
+        unpacked: Shape (N, K) dtype int8 with values in range [-8, 7]
+    """
+    # Convert int32 to uint8 view to extract nibbles
+    w_packed_uint8 = weight_packed.contiguous().view(torch.uint8)
+
+    # Each int32 = 4 bytes, each byte has 2 int4 values
+    # So shape (N, K/8) int32 -> (N, K/8, 4) uint8 -> (N, K/2) uint8
+    n, k_div_8 = weight_packed.shape
+    w_packed_uint8 = w_packed_uint8.view(n, k_div_8 * 4)
+
+    # Allocate output: (N, K) where K = K_div_8 * 8
+    k = k_div_8 * 8
+    w_unpacked = torch.zeros(n, k, dtype=torch.int8)
+
+    # Extract low and high nibbles
+    w_unpacked[:, 0::2] = (w_packed_uint8 & 0x0F).to(torch.int8)
+    w_unpacked[:, 1::2] = (w_packed_uint8 >> 4).to(torch.int8)
+
+    # Convert from uint4 [0, 15] to int4 [-8, 7]
+    # Values > 7 should be interpreted as negative
+    w_unpacked[w_unpacked > 7] -= 16
+
+    return w_unpacked.contiguous()
+
+
+def pack_int4_to_int32_gptq(weight_unpacked: torch.Tensor) -> torch.Tensor:
+    """
+    Pack int8 tensor (with int4 values) into int32 GPTQ format.
+
+    Args:
+        weight_unpacked: Shape (N, K) dtype int8 with values in range [-8, 7]
+
+    Returns:
+        packed: Shape (N, K/8) dtype int32
+    """
+    n, k = weight_unpacked.shape
+    assert k % 8 == 0, "K must be divisible by 8"
+
+    # Convert int4 [-8, 7] to uint4 [0, 15]
+    w_uint4 = weight_unpacked.clone()
+    w_uint4[w_uint4 < 0] += 16
+    w_uint4 = w_uint4.to(torch.uint8)
+
+    # Pack 2 uint4 into 1 uint8
+    w_packed_uint8 = torch.zeros(n, k // 2, dtype=torch.uint8)
+    w_packed_uint8 = (w_uint4[:, 1::2] << 4) | (w_uint4[:, 0::2])
+
+    # Reshape to int32
+    w_packed_int32 = (
+        w_packed_uint8.view(n, k // 8, 4).view(torch.uint8).view(n, k // 8).view(torch.int32)
+    )
+
+    return w_packed_int32.contiguous()
+
+
+def convert_compressed_tensor_to_gptq(
+    weight_packed: torch.Tensor,
+    weight_scale: torch.Tensor,
+    weight_shape: list[int],
+    group_size: int = 32,
+) -> dict[str, torch.Tensor]:
+    """
+    Convert compressed-tensors format to GPTQ format for TRT-LLM.
+
+    Args:
+        weight_packed: Shape (N, K/8) dtype int32
+        weight_scale: Shape (N, K/group_size) dtype bfloat16
+        weight_shape: [N, K] - original weight shape
+        group_size: Quantization group size
+
+    Returns:
+        Dictionary with:
+            - qweight: Shape (K/8, N) dtype int32 (transposed!)
+            - scales: Shape (K/group_size, N) dtype fp16 (transposed!)
+            - qzeros: Shape (K/group_size, N/8) dtype int32 (for symmetric, all zeros)
+    """
+    n, k = weight_shape
+
+    # TRT-LLM expects weights transposed: (K, N) instead of (N, K)
+    # But packed format keeps the packing dimension, so:
+    # Source: (N, K/8) -> Target: (K/8, N)
+    qweight = weight_packed.t().contiguous()
+
+    # Scales also need transpose: (N, K/group_size) -> (K/group_size, N)
+    scales = weight_scale.t().contiguous().to(torch.float16)
+
+    # For symmetric quantization, we use zero as the zero-point
+    # GPTQ format expects qzeros in packed format
+    # Shape: (K/group_size, N/8) since zeros are also packed 8 per int32
+    num_groups = k // group_size
+    # Create zeros tensor - for symmetric quantization, zero-point is 8 (middle of [0,15])
+    # Pack as uint4: each int32 contains 8 nibbles of value 8
+    # Create as bytes first then view as int32
+    qzeros_uint8 = torch.full((num_groups, n // 2), 0x88, dtype=torch.uint8)
+    qzeros = qzeros_uint8.view(torch.int32).contiguous()
+
+    return {
+        "qweight": qweight,
+        "scales": scales,
+        "qzeros": qzeros,
+    }
+
+
+def convert_checkpoint(
+    input_dir: str,
+    output_dir: str,
+    num_shards: int | None = None,
+    skip_existing: bool = True,
+):
+    """
+    Convert all shards from compressed-tensors to GPTQ format.
+
+    Args:
+        input_dir: Source checkpoint directory
+        output_dir: Output checkpoint directory
+        num_shards: Number of shards to process (None = all)
+        skip_existing: Skip conversion if output shard already exists
+    """
+    input_path = Path(input_dir)
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    # Find all safetensors files
+    shard_files = sorted(input_path.glob("model-*.safetensors"))
+    if not shard_files:
+        raise ValueError(f"No model shards found in {input_dir}")
+
+    if num_shards is not None:
+        shard_files = shard_files[:num_shards]
+
+    print(f"Found {len(shard_files)} shards to process")
+
+    # Track weight mapping for index file
+    new_weight_map = {}
+
+    # Load and convert each shard
+    for shard_idx, shard_file in enumerate(tqdm(shard_files, desc="Processing shards")):
+        shard_name = shard_file.name
+        output_file = output_path / shard_name
+
+        # Check if output already exists
+        if skip_existing and output_file.exists():
+            print(f"\n⏭️  Skipping {shard_name} (already exists)")
+            # Still need to build the weight_map from existing file
+            with safetensors.safe_open(str(output_file), framework="pt", device="cpu") as f:
+                for key in f:
+                    new_weight_map[key] = shard_name
+            continue
+
+        print(f"\n🔄 Converting {shard_file.name}...")
+
+        # Load source shard
+        source_tensors = {}
+        with safetensors.safe_open(str(shard_file), framework="pt", device="cpu") as f:
+            for key in f:
+                source_tensors[key] = f.get_tensor(key)
+
+        # Convert tensors
+        output_tensors = {}
+
+        for key, tensor in tqdm(source_tensors.items(), desc="Converting tensors", leave=False):
+            if key.endswith(".weight_packed"):
+                # This is a quantized weight - convert to GPTQ format
+                base_key = key[: -len(".weight_packed")]
+                scale_key = base_key + ".weight_scale"
+                shape_key = base_key + ".weight_shape"
+
+                if scale_key in source_tensors and shape_key in source_tensors:
+                    weight_shape = source_tensors[shape_key].tolist()
+
+                    # Convert to GPTQ format
+                    gptq_tensors = convert_compressed_tensor_to_gptq(
+                        weight_packed=tensor,
+                        weight_scale=source_tensors[scale_key],
+                        weight_shape=weight_shape,
+                        group_size=32,
+                    )
+
+                    # Save with GPTQ naming convention and track in weight_map
+                    qweight_key = base_key + ".qweight"
+                    scales_key = base_key + ".scales"
+                    qzeros_key = base_key + ".qzeros"
+
+                    output_tensors[qweight_key] = gptq_tensors["qweight"]
+                    output_tensors[scales_key] = gptq_tensors["scales"]
+                    output_tensors[qzeros_key] = gptq_tensors["qzeros"]
+
+                    new_weight_map[qweight_key] = shard_name
+                    new_weight_map[scales_key] = shard_name
+                    new_weight_map[qzeros_key] = shard_name
+                else:
+                    print(f"Warning: Missing scale or shape for {key}")
+
+            elif key.endswith((".weight_scale", ".weight_shape")):
+                # Skip these as they're handled above
+                continue
+            else:
+                # Keep non-quantized tensors as-is
+                output_tensors[key] = tensor
+                new_weight_map[key] = shard_name
+
+        # Save converted shard
+        safetensors.torch.save_file(output_tensors, str(output_file))
+        print(f"✅ Saved to {output_file}")
+
+    # Copy config.json and update quantization settings
+    config_file = input_path / "config.json"
+    if config_file.exists():
+        with open(config_file) as f:
+            config = json.load(f)
+
+        # Remove HuggingFace quantization_config if present
+        config.pop("quantization_config", None)
+
+        # Add TRT-LLM native quantization config for GPTQ
+        config["quantization"] = {
+            "quant_algo": "W4A16_GPTQ",  # TRT-LLM's enum value
+            "group_size": 32,
+            "has_zero_point": True,  # GPTQ uses asymmetric quantization
+            "pre_quant_scale": False,  # No pre-quantization scaling
+        }
+
+        output_config_file = output_path / "config.json"
+        with open(output_config_file, "w") as f:
+            json.dump(config, f, indent=2)
+        print(f"\nSaved config to {output_config_file}")
+
+    # Generate new safetensors index file
+    index_data = {
+        "metadata": {
+            "total_size": sum(
+                (output_path / shard_file.name).stat().st_size for shard_file in shard_files
+            )
+        },
+        "weight_map": new_weight_map,
+    }
+
+    index_file = output_path / "model.safetensors.index.json"
+    with open(index_file, "w") as f:
+        json.dump(index_data, f, indent=2)
+    print(f"\nGenerated index file: {index_file}")
+    print(f"  Total tensors: {len(new_weight_map)}")
+
+    # Copy other necessary files
+    import shutil
+
+    # JSON files (tokenizer and generation config)
+    for file in [
+        "generation_config.json",
+        "tokenizer.json",
+        "tokenizer_config.json",
+        "special_tokens_map.json",
+        "vocab.json",
+        "merges.txt",
+    ]:
+        src = input_path / file
+        if src.exists():
+            shutil.copy(src, output_path / file)
+            print(f"Copied: {file}")
+
+    # Python files (model architecture, custom tokenizers)
+    for file in ["configuration_deepseek.py", "modeling_deepseek.py", "tokenization_kimi.py"]:
+        src = input_path / file
+        if src.exists():
+            shutil.copy(src, output_path / file)
+            print(f"Copied: {file}")
+
+    # Tokenizer model files
+    for file in ["tiktoken.model", "tokenizer.model", "sentencepiece.model"]:
+        src = input_path / file
+        if src.exists():
+            shutil.copy(src, output_path / file)
+            print(f"Copied: {file}")
+
+    # Template files
+    for file in ["chat_template.jinja", "chat_template.json"]:
+        src = input_path / file
+        if src.exists():
+            shutil.copy(src, output_path / file)
+            print(f"Copied: {file}")
+
+    print(f"\n✓ Conversion complete! Output saved to {output_dir}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert Kimi-K2 checkpoint to TRT-LLM GPTQ format"
+    )
+    parser.add_argument(
+        "--input-dir",
+        type=str,
+        default="/home/scratch.omniml_data_1/models/Kimi-K2-Thinking",
+        help="Input checkpoint directory with compressed-tensors format",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="/home/scratch.omniml_data_2/zhiyuc/checkpoints/Kimi-K2-Thinking-GPTQ",
+        help="Output directory for GPTQ format checkpoint",
+    )
+    parser.add_argument(
+        "--num-shards",
+        type=int,
+        default=None,
+        help="Number of shards to convert (default: all)",
+    )
+    parser.add_argument(
+        "--no-skip-existing",
+        action="store_true",
+        help="Re-convert shards even if they already exist (default: skip existing)",
+    )
+
+    args = parser.parse_args()
+
+    convert_checkpoint(
+        input_dir=args.input_dir,
+        output_dir=args.output_dir,
+        num_shards=args.num_shards,
+        skip_existing=not args.no_skip_existing,
+    )
+
+
+if __name__ == "__main__":
+    main()