diff --git a/python/mlc_llm/conversation_template/__init__.py b/python/mlc_llm/conversation_template/__init__.py
index 9873062ac7..5a679bf7b2 100644
--- a/python/mlc_llm/conversation_template/__init__.py
+++ b/python/mlc_llm/conversation_template/__init__.py
@@ -20,6 +20,7 @@
     llava,
     mistral,
     oasst,
+    olmo,
     orion,
     phi,
     qwen2,
diff --git a/python/mlc_llm/conversation_template/olmo.py b/python/mlc_llm/conversation_template/olmo.py
new file mode 100644
index 0000000000..17089dc16a
--- /dev/null
+++ b/python/mlc_llm/conversation_template/olmo.py
@@ -0,0 +1,28 @@
+"""OLMo default templates"""
+
+from mlc_llm.protocol.conversation_protocol import Conversation, MessagePlaceholders
+
+from .registry import ConvTemplateRegistry
+
+# Note that eos_token id is "50279" both in Allenai and AMD version.
+# So use the number instead of text.
+# Allenai version chat_template and eos_token:
+# https://huggingface.co/allenai/OLMo-7B-Instruct/blob/main/tokenizer_config.json
+# AMD version chat_template and eos_token:
+# https://huggingface.co/amd/AMD-OLMo-1B-SFT-DPO/blob/main/tokenizer_config.json
+ConvTemplateRegistry.register_conv_template(
+    Conversation(
+        name="olmo",
+        system_template=f"{MessagePlaceholders.SYSTEM.value}",
+        system_message="",
+        system_prefix_token_ids=[50279],
+        roles={
+            "user": "<|user|>",
+            "assistant": "<|assistant|>",
+        },
+        seps=["\n"],
+        role_content_sep="\n",
+        role_empty_sep="\n",
+        stop_token_ids=[50279],
+    )
+)
diff --git a/python/mlc_llm/interface/gen_config.py b/python/mlc_llm/interface/gen_config.py
index 62d16100c9..38cc91de21 100644
--- a/python/mlc_llm/interface/gen_config.py
+++ b/python/mlc_llm/interface/gen_config.py
@@ -306,4 +306,5 @@ def gen_config(  # pylint: disable=too-many-locals,too-many-arguments,too-many-b
     "aya-23",
     "deepseek_v2",
     "deepseek",
+    "olmo",
 }
diff --git a/python/mlc_llm/model/model.py b/python/mlc_llm/model/model.py
index f5b05763ed..383ad600ba 100644
--- a/python/mlc_llm/model/model.py
+++ b/python/mlc_llm/model/model.py
@@ -28,6 +28,7 @@
 from .minicpm import minicpm_loader, minicpm_model, minicpm_quantization
 from .mistral import mistral_loader, mistral_model, mistral_quantization
 from .mixtral import mixtral_loader, mixtral_model, mixtral_quantization
+from .olmo import olmo_loader, olmo_model, olmo_quantization
 from .orion import orion_loader, orion_model, orion_quantization
 from .phi import phi_loader, phi_model, phi_quantization
 from .phi3 import phi3_loader, phi3_model, phi3_quantization
@@ -532,4 +533,21 @@ class Model:
             "ft-quant": deepseek_quantization.ft_quant,
         },
     ),
+    "olmo": Model(
+        name="olmo",
+        model=olmo_model.OLMoForCausalLM,
+        config=olmo_model.OLMoConfig,
+        source={
+            "huggingface-torch": olmo_loader.huggingface,
+            "huggingface-safetensor": olmo_loader.huggingface,
+            "awq": olmo_loader.awq,
+        },
+        quantize={
+            "no-quant": olmo_quantization.no_quant,
+            "group-quant": olmo_quantization.group_quant,
+            "ft-quant": olmo_quantization.ft_quant,
+            "awq": olmo_quantization.awq_quant,
+            "per-tensor-quant": olmo_quantization.per_tensor_quant,
+        },
+    ),
 }
diff --git a/python/mlc_llm/model/olmo/__init__.py b/python/mlc_llm/model/olmo/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/python/mlc_llm/model/olmo/olmo_loader.py b/python/mlc_llm/model/olmo/olmo_loader.py
new file mode 100644
index 0000000000..3d1a7c037c
--- /dev/null
+++ b/python/mlc_llm/model/olmo/olmo_loader.py
@@ -0,0 +1,172 @@
+"""
+This file specifies how MLC's OLMo parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+import numpy as np
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.quantization import Quantization
+
+from .olmo_model import OLMoConfig, OLMoForCausalLM
+from .olmo_quantization import awq_quant
+
+
+def huggingface(model_config: OLMoConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of HuggingFace PyTorch parameters.
+
+    Parameters
+    ----------
+    model_config : OLMoConfig
+        The configuration of the OLMo model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to HuggingFace PyTorch.
+    """
+    model = OLMoForCausalLM(model_config)
+    if quantization is not None:
+        model.to(quantization.model_dtype)
+    _, _named_params, _ = model.export_tvm(  # type: ignore[misc]
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        mlc_name = f"{attn}.qkv_proj.weight"
+        mlc_param = named_parameters[mlc_name]
+        mapping.add_mapping(
+            mlc_name,
+            [
+                f"{attn}.q_proj.weight",
+                f"{attn}.k_proj.weight",
+                f"{attn}.v_proj.weight",
+            ],
+            functools.partial(
+                lambda q, k, v, dtype: np.concatenate([q, k, v], axis=0).astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+        # Add gates in MLP
+        mlp = f"model.layers.{i}.mlp"
+        mlc_name = f"{mlp}.gate_up_proj.weight"
+        mlc_param = named_parameters[mlc_name]
+        mapping.add_mapping(
+            mlc_name,
+            [
+                f"{mlp}.gate_proj.weight",
+                f"{mlp}.up_proj.weight",
+            ],
+            functools.partial(
+                lambda gate, up, dtype: np.concatenate([gate, up], axis=0).astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+        # inv_freq is not used in the model
+        mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            mapping.add_mapping(
+                mlc_name,
+                [mlc_name],
+                functools.partial(
+                    lambda x, dtype: x.astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+    return mapping
+
+
+def awq(model_config: OLMoConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of AWQ parameters.
+    Parameters
+    ----------
+    model_config : OLMoConfig
+        The configuration of the OLMo model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to AWQ.
+    """
+    model, _ = awq_quant(model_config, quantization)
+    _, _named_params, _ = model.export_tvm(  # type: ignore[misc]
+        spec=model.get_default_spec(),  # type: ignore[attr-defined]
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            mlc_name = f"{attn}.qkv_proj.{quantize_suffix}"
+            assert mlc_name in named_parameters
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{attn}.q_proj.{quantize_suffix}",
+                    f"{attn}.k_proj.{quantize_suffix}",
+                    f"{attn}.v_proj.{quantize_suffix}",
+                ],
+                functools.partial(
+                    lambda q, k, v, dtype: np.concatenate(
+                        [q, k, v],
+                        axis=1,  # AWQ GEMM would transpose the weight
+                    ).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+        # Concat gate and up in MLP
+        mlp = f"model.layers.{i}.mlp"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            mlc_name = f"{mlp}.gate_up_proj.{quantize_suffix}"
+            assert mlc_name in named_parameters
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{mlp}.gate_proj.{quantize_suffix}",
+                    f"{mlp}.up_proj.{quantize_suffix}",
+                ],
+                functools.partial(
+                    lambda gate, up, dtype: np.concatenate(
+                        [gate, up],
+                        axis=1,  # AWQ GEMM would transpose the weight
+                    ).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+        # inv_freq is not used in the model
+        mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            mapping.add_mapping(
+                mlc_name,
+                [mlc_name],
+                functools.partial(lambda x, dtype: x.astype(dtype), dtype=mlc_param.dtype),
+            )
+    return mapping
diff --git a/python/mlc_llm/model/olmo/olmo_model.py b/python/mlc_llm/model/olmo/olmo_model.py
new file mode 100644
index 0000000000..06f00a0b6a
--- /dev/null
+++ b/python/mlc_llm/model/olmo/olmo_model.py
@@ -0,0 +1,588 @@
+"""
+Implementation for OLMo architecture.
+TODO: add docstring
+"""
+
+import dataclasses
+from functools import partial
+from typing import Any, Dict, Optional
+
+from tvm import te, tir
+from tvm.relax.frontend import nn
+from tvm.relax.frontend.nn import Tensor, op
+
+from mlc_llm import op as op_ext
+from mlc_llm.nn import PagedKVCache, RopeMode
+from mlc_llm.support import logging
+from mlc_llm.support import tensor_parallel as tp
+from mlc_llm.support.config import ConfigBase
+from mlc_llm.support.style import bold
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class OLMoConfig(ConfigBase):  # pylint: disable=too-many-instance-attributes
+    """Configuration of the OLMo model."""
+
+    vocab_size: int = None
+    hidden_size: int = None
+    num_attention_heads: int = None
+    num_key_value_heads: int = 0
+    head_dim: int = 0
+    position_embedding_base: int = 0
+    rope_scaling: Optional[Dict[str, Any]] = None
+    intermediate_size: int = None
+    hidden_act: str = None
+    num_hidden_layers: int = None
+    tie_word_embeddings: bool = False
+    context_window_size: int = 0
+    prefill_chunk_size: int = 0
+    tensor_parallel_shards: int = 1
+    pipeline_parallel_stages: int = 1
+    max_batch_size: int = 1
+    clip_qkv: float = None
+    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)
+
+    def __post_init__(self):  # pylint: disable=too-many-branches
+        if self.num_key_value_heads == 0:
+            self.num_key_value_heads = self.num_attention_heads
+        if self.head_dim == 0:
+            self.head_dim = self.hidden_size // self.num_attention_heads
+        assert self.num_attention_heads % self.num_key_value_heads == 0
+
+        if self.position_embedding_base == 0:
+            if "rope_theta" in self.kwargs:
+                self.position_embedding_base = self.kwargs.pop("rope_theta")
+            else:
+                self.position_embedding_base = 10000
+
+        if self.context_window_size == 0:
+            for name in ["max_position_embeddings", "max_sequence_length"]:
+                if name in self.kwargs:
+                    self.context_window_size = self.kwargs.pop(name)
+                    logger.info(
+                        "%s not found in config.json. Falling back to %s (%d)",
+                        bold("context_window_size"),
+                        bold(name),
+                        self.context_window_size,
+                    )
+                    break
+            else:
+                raise ValueError(
+                    "Unable to determine the maximum sequence length, because none of "
+                    "`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
+                    "provided in `config.json`."
+                )
+
+        if self.prefill_chunk_size == 0:
+            logger.info(
+                "%s defaults to %d", bold("prefill_chunk_size"), min(self.context_window_size, 8192)
+            )
+            self.prefill_chunk_size = min(self.context_window_size, 8192)
+        elif self.prefill_chunk_size > self.context_window_size:
+            logger.info(
+                "Overriding %s from %d to %d",
+                bold("prefill_chunk_size"),
+                self.prefill_chunk_size,
+                min(self.context_window_size, 8192),
+            )
+            self.prefill_chunk_size = min(self.context_window_size, 8192)
+
+        if (
+            self.pipeline_parallel_stages <= 0
+            or self.pipeline_parallel_stages > self.num_hidden_layers
+        ):
+            raise ValueError(
+                f'Invalid "pipeline_parallel_stages" value({self.pipeline_parallel_stages}). '
+            )
+
+        if self.clip_qkv is not None:
+            if self.clip_qkv <= 0:
+                raise ValueError(f"'clip_qkv'({self.clip_qkv}) should be non-negative")
+
+
+class OLMoEebedding(nn.Embedding):
+    """The embedding module that can be shared with the final lm_head. From Qwen2Embedding."""
+
+    def lm_head_forward(self, x: nn.Tensor):
+        """The lm_head forwarding, which transposes the weight and multiplies
+        with the input tensor.
+        """
+        weight = nn.op.permute_dims(self.weight)
+        return nn.op.matmul(x, weight, out_dtype="float32")
+
+
+class OLMoAttention(nn.Module):  # pylint: disable=missing-class-docstring
+    def __init__(self, config: OLMoConfig):
+        self.num_q_heads = config.num_attention_heads // config.tensor_parallel_shards
+        assert (
+            config.num_key_value_heads >= config.tensor_parallel_shards
+        ), f"Too large tensor_parallel_shards, must be smaller than {config.num_key_value_heads}"
+        assert (
+            config.num_key_value_heads % config.tensor_parallel_shards == 0
+        ), f"num_kv_heads({config.num_key_value_heads}) must be divisible by tensor_parallel_shards"
+        self.num_kv_heads = config.num_key_value_heads // config.tensor_parallel_shards
+        self.head_dim = config.head_dim
+        self.qkv_proj = nn.Linear(
+            in_features=config.hidden_size,
+            out_features=(self.num_q_heads + 2 * self.num_kv_heads) * self.head_dim,
+            bias=False,
+        )
+        self.clip_qkv = config.clip_qkv
+        self.o_proj = nn.Linear(
+            in_features=self.num_q_heads * self.head_dim,
+            out_features=config.hidden_size,
+            bias=False,
+        )
+
+    def forward(  # pylint: disable=missing-function-docstring
+        self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int
+    ):
+        d, h_q, h_kv = self.head_dim, self.num_q_heads, self.num_kv_heads
+        b, s, _ = hidden_states.shape
+
+        # QKV Projection
+        qkv = self.qkv_proj(hidden_states)
+
+        # Clamp after qkv projection if needed
+        if self.clip_qkv is not None:
+            qkv = qkv.maximum(-self.clip_qkv).minimum(self.clip_qkv)
+
+        qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
+        # Attention
+        output = op.reshape(
+            paged_kv_cache.attention_with_fused_qkv(layer_id, qkv, self.num_q_heads),
+            (b, s, h_q * d),
+        )
+        return self.o_proj(output)
+
+
+# Copied from qwen2_model.ACT2FN
+ACT2FN = {
+    "gelu": partial(nn.gelu, approximate=False),
+    "relu": nn.relu,
+    "silu": nn.silu,
+    "swish": nn.silu,
+    "gelu_new": partial(nn.gelu, approximate=True),
+}
+
+
+class OLMoFFN(nn.Module):  # pylint: disable=missing-class-docstring
+    def __init__(self, config: OLMoConfig):
+        super().__init__()
+        if config.intermediate_size % config.tensor_parallel_shards != 0:
+            raise ValueError(
+                f"Cannot split MLP intermediate size {config.intermediate_size} "
+                f"evenly to {config.tensor_parallel_shards} GPUs."
+            )
+        self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards
+        self.gate_up_proj = nn.Linear(
+            in_features=config.hidden_size,
+            out_features=2 * self.intermediate_size,
+            bias=False,
+        )
+        self.act_fn = ACT2FN[config.hidden_act]
+        self.down_proj = nn.Linear(
+            in_features=self.intermediate_size,
+            out_features=config.hidden_size,
+            bias=False,
+        )
+
+    def forward(self, x: Tensor):  # pylint: disable=missing-function-docstring
+        concat_x1_x2 = self.gate_up_proj(x)
+        x1, x2 = op.split(concat_x1_x2, 2, axis=-1)
+        return self.down_proj(self.act_fn(x1) * x2)
+
+
+# pylint: disable=trailing-whitespace
+
+
+class OLMoDecoderLayer(nn.Module):  # pylint: disable=missing-class-docstring
+    def __init__(self, config: OLMoConfig):
+        self.input_layernorm = nn.LayerNorm(
+            normalized_shape=config.hidden_size,
+            eps=1e-5,
+            elementwise_affine=False,
+        )
+        self.self_attn = OLMoAttention(config)
+        self.post_attention_layernorm = nn.LayerNorm(
+            normalized_shape=config.hidden_size,
+            eps=1e-5,
+            elementwise_affine=False,
+        )
+        self.mlp = OLMoFFN(config)
+
+        def _set_tp():
+            def _set(layer, hint):
+                layer.weight.attrs["shard_strategy"] = hint
+
+            hd = config.head_dim
+            q = self.self_attn.num_q_heads * hd
+            k = self.self_attn.num_kv_heads * hd
+            v = self.self_attn.num_kv_heads * hd
+            i = self.mlp.intermediate_size
+            _set(self.self_attn.qkv_proj, tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0))
+            _set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
+            _set(self.mlp.gate_up_proj, tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0))
+            _set(self.mlp.down_proj, tp.ShardSingleDim("_shard_mlp_down", dim=1))
+
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        _set_tp()
+
+    def _apply_residual(self, out, residual):
+        if self.tensor_parallel_shards > 1:
+            return op.ccl_allreduce(out, "sum") + residual
+        return out + residual
+
+    def forward(  # pylint: disable=missing-function-docstring
+        self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int
+    ):
+        out = self.self_attn(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        out = self.mlp(self.post_attention_layernorm(hidden_states))
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        return hidden_states
+
+
+class OLMoModel(nn.Module):  # pylint: disable=missing-class-docstring
+    def __init__(self, config: OLMoConfig):
+        assert config.hidden_size % config.num_attention_heads == 0
+        self.embed_tokens = OLMoEebedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList(
+            [OLMoDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.norm = nn.LayerNorm(
+            normalized_shape=config.hidden_size,
+            eps=1e-5,
+            elementwise_affine=False,
+        )
+
+        self.num_layers_per_stage = (
+            config.num_hidden_layers + config.pipeline_parallel_stages - 1
+        ) // config.pipeline_parallel_stages
+        # Compute pipeline layer partition.
+        layers_per_stage = (
+            config.num_hidden_layers + config.pipeline_parallel_stages - 1
+        ) // config.pipeline_parallel_stages
+        self.layer_partition = [
+            i * layers_per_stage for i in range(config.pipeline_parallel_stages)
+        ] + [config.num_hidden_layers]
+
+    def forward(  # pylint: disable=missing-function-docstring
+        self, inputs: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        hidden_states = inputs
+        for layer_id, layer in enumerate(self.layers):
+            if layer_id != 0 and layer_id in self.layer_partition:
+                hidden_states = op_ext.pipeline_stage_boundary(hidden_states)
+            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class OLMoForCausalLM(  # pylint: disable=missing-class-docstring,too-many-instance-attributes
+    nn.Module
+):
+    def __init__(self, config: OLMoConfig):
+        self.model = OLMoModel(config)
+        self.tie_word_embeddings = config.tie_word_embeddings
+        if not config.tie_word_embeddings:
+            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.vocab_size = config.vocab_size
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.rope_theta = config.position_embedding_base
+        self.rope_scaling = config.rope_scaling
+        self.intermediate_size = config.intermediate_size
+        self.num_hidden_layers = config.num_hidden_layers
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        self.dtype = "float32"
+
+        def _set_pp():
+            # hidden layers
+            for layer_id in range(config.num_hidden_layers):
+                stage = layer_id // (config.num_hidden_layers // config.pipeline_parallel_stages)
+                for _, param in self.model.layers[layer_id].named_parameters():
+                    param.attrs["pipeline_stages"] = [stage]
+
+            # embedding table and lm_head is required by all stages
+            all_stages = list(range(config.pipeline_parallel_stages))
+            self.model.embed_tokens.weight.attrs["pipeline_stages"] = all_stages
+            if not config.tie_word_embeddings:
+                self.lm_head.weight.attrs["pipeline_stages"] = all_stages
+
+        _set_pp()
+
+    def to(self, dtype: Optional[str] = None):
+        super().to(dtype=dtype)
+        if dtype is not None:
+            self.dtype = dtype
+
+    def batch_forward(  # pylint: disable=missing-function-docstring
+        self,
+        input_embeds: Tensor,
+        paged_kv_cache: PagedKVCache,
+        logit_positions: Optional[Tensor] = None,
+    ):
+        op_ext.configure()
+        hidden_states = self.model(input_embeds, paged_kv_cache)
+        if logit_positions is not None:
+            if self.tensor_parallel_shards > 1:
+                logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
+            hidden_states = op.take(hidden_states, logit_positions, axis=1)
+        return self.get_logits(hidden_states)
+
+    def batch_forward_to_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self,
+        input_embeds: Tensor,
+        paged_kv_cache: PagedKVCache,
+    ):
+        op_ext.configure()
+        hidden_states = self.model(input_embeds, paged_kv_cache)
+        return hidden_states
+
+    def embed(self, input_ids: Tensor):  # pylint: disable=missing-function-docstring
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
+        return self.model.embed_tokens(input_ids)
+
+    def get_logits(self, hidden_states: Tensor):  # pylint: disable=missing-function-docstring
+        op_ext.configure()
+        if self.tie_word_embeddings:
+            logits = self.model.embed_tokens.lm_head_forward(hidden_states)
+        else:
+            logits = self.lm_head(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits
+
+    def batch_select_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self, hidden_states: Tensor, logit_positions: Tensor
+    ):
+        op_ext.configure()
+        if self.tensor_parallel_shards > 1:
+            logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
+        hidden_states = op.take(hidden_states, logit_positions, axis=0)
+        return hidden_states
+
+    def prefill(  # pylint: disable=missing-function-docstring
+        self, input_embed: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        op_ext.configure()
+
+        def _index(x: te.Tensor):  # get tensor of the last sequence
+            b, s, d = x.shape
+            return te.compute((b, 1, d), lambda i, _, k: x[i, s - 1, k])
+
+        # pylint: disable=trailing-whitespace
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        hidden_states = op.tensor_expr_op(_index, name_hint="index", args=[hidden_states])
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    # pylint: disable=trailing-whitespace
+    def decode(  # pylint: disable=missing-function-docstring
+        self, input_embed: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        op_ext.configure()
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def prefill_to_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self, input_embed: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        op_ext.configure()
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def decode_to_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self, input_embed: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        op_ext.configure()
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def batch_prefill(  # pylint: disable=missing-function-docstring
+        self, input_embeds: Tensor, logit_positions: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
+        return logits, paged_kv_cache
+
+    def batch_decode(  # pylint: disable=missing-function-docstring
+        self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def batch_verify(  # pylint: disable=missing-function-docstring
+        self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def batch_prefill_to_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def batch_decode_to_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def batch_verify_to_last_hidden_states(  # pylint: disable=missing-function-docstring
+        self, input_embeds: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        hidden_states = self.batch_forward_to_last_hidden_states(input_embeds, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def create_paged_kv_cache(  # pylint: disable=missing-function-docstring,too-many-arguments
+        self,
+        max_batch_size: tir.Var,
+        max_total_seq_len: tir.Var,
+        prefill_chunk_size: tir.Var,
+        page_size: tir.Var,
+        support_sliding_window: tir.Var,
+    ) -> PagedKVCache:
+        return PagedKVCache.create_generic(
+            max_batch_size=max_batch_size,
+            max_total_seq_len=max_total_seq_len,
+            prefill_chunk_size=prefill_chunk_size,
+            page_size=page_size,
+            support_sliding_window=support_sliding_window,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
+            num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
+            head_dim=self.head_dim,
+            rope_mode=RopeMode.NORMAL,
+            rope_scale=1,
+            rope_theta=self.rope_theta,
+            rope_scaling=self.rope_scaling,
+            layer_partition=self.model.layer_partition,
+            dtype=self.dtype,
+        )
+
+    def get_default_spec(self):  # pylint: disable=missing-function-docstring
+        mod_spec = {
+            "embed": {
+                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "get_logits": {
+                "hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_select_last_hidden_states": {
+                "hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
+                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
+                "$": {
+                    "param_mode": "none",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill": {
+                "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode": {
+                "input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill_to_last_hidden_states": {
+                "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode_to_last_hidden_states": {
+                "input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode": {
+                "input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_verify": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill_to_last_hidden_states": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode_to_last_hidden_states": {
+                "input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_verify_to_last_hidden_states": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "create_paged_kv_cache": {
+                "max_batch_size": int,
+                "max_total_seq_len": int,
+                "prefill_chunk_size": int,
+                "page_size": int,
+                "support_sliding_window": int,
+                "$": {
+                    "param_mode": "none",
+                    "effect_mode": "none",
+                },
+            },
+        }
+        return nn.spec.ModuleSpec.from_raw(mod_spec, self)
diff --git a/python/mlc_llm/model/olmo/olmo_quantization.py b/python/mlc_llm/model/olmo/olmo_quantization.py
new file mode 100644
index 0000000000..16e4f1f47b
--- /dev/null
+++ b/python/mlc_llm/model/olmo/olmo_quantization.py
@@ -0,0 +1,94 @@
+"""This file specifies how MLC's OLMo parameters are quantized using group quantization
+or other formats."""
+
+from typing import Tuple
+
+from tvm.relax.frontend import nn
+
+from mlc_llm.loader import QuantizeMapping
+from mlc_llm.quantization import (
+    AWQQuantize,
+    FTQuantize,
+    GroupQuantize,
+    NoQuantize,
+    PerTensorQuantize,
+)
+
+from .olmo_model import OLMoConfig, OLMoForCausalLM
+
+
+def group_quant(
+    model_config: OLMoConfig,
+    quantization: GroupQuantize,
+) -> Tuple[nn.Module, QuantizeMapping]:
+    """Quantize a OLMo-architecture model using group quantization."""
+    model: nn.Module = OLMoForCausalLM(model_config)
+    model.to(quantization.model_dtype)
+    quant_map = QuantizeMapping({}, {})
+    quantization.tensor_parallel_shards = model_config.tensor_parallel_shards
+    model = quantization.quantize_model(
+        model,
+        quant_map,
+        "",
+    )
+    return model, quant_map
+
+
+def ft_quant(
+    model_config: OLMoConfig,
+    quantization: FTQuantize,
+) -> Tuple[nn.Module, QuantizeMapping]:
+    """Quantize a OLMo-architecture model using FasterTransformer quantization."""
+    model: nn.Module = OLMoForCausalLM(model_config)
+    model.to(quantization.model_dtype)
+    quant_map = QuantizeMapping({}, {})
+    model = quantization.quantize_model(
+        model,
+        quant_map,
+        "",
+    )
+    return model, quant_map
+
+
+def awq_quant(
+    model_config: OLMoConfig,
+    quantization: AWQQuantize,
+) -> Tuple[nn.Module, QuantizeMapping]:
+    """Quantize a OLMo-architecture model using Activation-aware Weight Quantization(AWQ)."""
+    model: nn.Module = OLMoForCausalLM(model_config)
+    model.to(quantization.model_dtype)
+    quant_map = QuantizeMapping({}, {})
+    model = quantization.quantize_model(
+        model,
+        quant_map,
+        "",
+    )
+    return model, quant_map
+
+
+def no_quant(
+    model_config: OLMoConfig,
+    quantization: NoQuantize,
+) -> Tuple[nn.Module, QuantizeMapping]:
+    """Quantize a OLMo model without quantization."""
+    model: nn.Module = OLMoForCausalLM(model_config)
+    model.to(quantization.model_dtype)
+    quant_map = QuantizeMapping({}, {})
+    return model, quant_map
+
+
+def per_tensor_quant(
+    model_config: OLMoConfig,
+    quantization: PerTensorQuantize,
+) -> Tuple[nn.Module, QuantizeMapping]:
+    """Quantize a OLMo-architecture model using per-tensor quantization."""
+    model: nn.Module = OLMoForCausalLM(model_config)
+    model.to(quantization.model_dtype)
+    quant_map = QuantizeMapping({}, {})
+    model = quantization.quantize_model(
+        model,
+        quant_map,
+        "",
+        tensor_parallel_shards=model_config.tensor_parallel_shards,
+    )
+    return model, quant_map