diff --git a/docs/supported_models/generative_models.md b/docs/supported_models/generative_models.md
index 3d75fa307..e86cd4874 100644
--- a/docs/supported_models/generative_models.md
+++ b/docs/supported_models/generative_models.md
@@ -58,6 +58,7 @@ in the GitHub search bar.
 | **Ling** (16.8B–290B) | `inclusionAI/Ling-lite`, `inclusionAI/Ling-plus` | InclusionAI’s open MoE models. Ling-Lite has 16.8B total / 2.75B active parameters, and Ling-Plus has 290B total / 28.8B active parameters. They are designed for high performance on NLP and complex reasoning tasks. |
 | **Granite 3.0, 3.1** (IBM)               | `ibm-granite/granite-3.1-8b-instruct`                          | IBM's open dense foundation models optimized for reasoning, code, and business AI use cases. Integrated with Red Hat and watsonx systems. |
 | **Granite 3.0 MoE** (IBM)               | `ibm-granite/granite-3.0-3b-a800m-instruct`                          | IBM’s Mixture-of-Experts models offering strong performance with cost-efficiency. MoE expert routing designed for enterprise deployment at scale. |
+| **GPT-J** (6B)                    | `EleutherAI/gpt-j-6b`                             | EleutherAI's GPT-2-like causal language model (6B) trained on the [Pile](https://pile.eleuther.ai/) dataset. |
 | **Orion** (14B)               | `OrionStarAI/Orion-14B-Base`                         | A series of open-source multilingual large language models by OrionStarAI, pretrained on a 2.5T token multilingual corpus including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. |
 | **Llama Nemotron Super** (v1, v1.5, NVIDIA) | `nvidia/Llama-3_3-Nemotron-Super-49B-v1`, `nvidia/Llama-3_3-Nemotron-Super-49B-v1_5` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. |
 | **Llama Nemotron Ultra** (v1, NVIDIA) | `nvidia/Llama-3_1-Nemotron-Ultra-253B-v1` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. |
diff --git a/python/sglang/srt/models/gpt_j.py b/python/sglang/srt/models/gpt_j.py
new file mode 100644
index 000000000..085c8c536
--- /dev/null
+++ b/python/sglang/srt/models/gpt_j.py
@@ -0,0 +1,326 @@
+# Copyright 2023-2025 SGLang Team
+# 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.
+# ==============================================================================
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/gpt_j.py
+"""Inference-only GPT-J model compatible with HuggingFace weights."""
+
+from typing import Iterable, Optional, Tuple, Type
+
+import torch
+from torch import nn
+from transformers import GPTJConfig
+
+from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_world_size
+from sglang.srt.layers.activation import get_act_fn
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
+from sglang.srt.utils import add_prefix
+
+
+class GPTJAttention(nn.Module):
+
+    def __init__(
+        self,
+        layer_id: int,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        total_num_heads = config.num_attention_heads
+        hidden_size = config.hidden_size
+        head_dim = hidden_size // total_num_heads
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            head_dim,
+            total_num_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("qkv_proj", prefix),
+        )
+        self.out_proj = RowParallelLinear(
+            hidden_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("out_proj", prefix),
+        )
+
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        assert total_num_heads % tensor_model_parallel_world_size == 0
+        num_heads = total_num_heads // tensor_model_parallel_world_size
+
+        scaling = head_dim**-0.5
+        assert getattr(config, "rotary", True)
+        assert config.rotary_dim % 2 == 0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        self.rotary_emb = get_rope(
+            head_dim,
+            rotary_dim=config.rotary_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            is_neox_style=False,
+        )
+        self.attn = RadixAttention(
+            num_heads,
+            head_dim,
+            scaling=scaling,
+            num_kv_heads=num_heads,
+            layer_id=layer_id,
+            quant_config=quant_config,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, forward_batch)
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output
+
+
+class GPTJMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.fc_in = ColumnParallelLinear(
+            hidden_size,
+            intermediate_size,
+            quant_config=quant_config,
+            prefix=add_prefix("fc_in", prefix),
+        )
+        self.fc_out = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            quant_config=quant_config,
+            prefix=add_prefix("fc_out", prefix),
+        )
+
+        self.act = get_act_fn(config.activation_function)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc_out(hidden_states)
+        return hidden_states
+
+
+class GPTJBlock(nn.Module):
+
+    def __init__(
+        self,
+        layer_id: int,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = GPTJAttention(
+            layer_id,
+            config,
+            quant_config,
+            prefix=add_prefix("attn", prefix),
+        )
+        self.mlp = GPTJMLP(
+            inner_dim,
+            config,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+        )
+        mlp_output = self.mlp(hidden_states)
+        hidden_states = attn_output + mlp_output + residual
+        return hidden_states
+
+
+class GPTJModel(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        embed_dim = config.n_embd
+        self.wte = VocabParallelEmbedding(
+            config.vocab_size,
+            embed_dim,
+        )
+        self.h = nn.ModuleList(
+            [
+                GPTJBlock(
+                    i,
+                    config,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"h.{i}", prefix),
+                )
+                for i in range(config.n_layer)
+            ]
+        )
+        self.ln_f = nn.LayerNorm(embed_dim, eps=config.layer_norm_epsilon)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.wte(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+        else:
+            hidden_states = self.get_input_embeddings(input_ids)
+
+        for layer in self.h:
+            hidden_states = layer(positions, hidden_states, forward_batch)
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPTJForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: GPTJConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        assert not config.tie_word_embeddings
+        self.quant_config = quant_config
+        self.transformer = GPTJModel(
+            config,
+            quant_config,
+            prefix=add_prefix("transformer", prefix),
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.n_embd,
+            bias=True,
+            quant_config=quant_config,
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(
+            input_ids, positions, forward_batch, inputs_embeds
+        )
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "attn.bias" in name or "attn.masked_bias" in name:
+                continue
+
+            if self.quant_config is not None and (
+                scale_name := self.quant_config.get_cache_scale(name)
+            ):
+                # Loading kv cache quantization scales
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                loaded_weight = (
+                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
+                )
+                weight_loader(param, loaded_weight)
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = GPTJForCausalLM