diff --git a/python/sglang/srt/configs/internvl.py b/python/sglang/srt/configs/internvl.py
index 7033ef359..3ba9c61c1 100644
--- a/python/sglang/srt/configs/internvl.py
+++ b/python/sglang/srt/configs/internvl.py
@@ -6,11 +6,13 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import sentencepiece as spm
 from transformers import (
     TOKENIZER_MAPPING,
+    GptOssConfig,
     LlamaConfig,
     PretrainedConfig,
     PreTrainedTokenizer,
     Qwen2Config,
     Qwen3Config,
+    Qwen3MoeConfig,
 )
 
 from sglang.utils import logger
@@ -316,7 +318,11 @@ class InternVLChatConfig(PretrainedConfig):
         elif llm_config.get("architectures")[0] == "Qwen2ForCausalLM":
             self.llm_config = Qwen2Config(**llm_config)
         elif llm_config.get("architectures")[0] == "Qwen3MoeForCausalLM":
+            self.llm_config = Qwen3MoeConfig(**llm_config)
+        elif llm_config.get("architectures")[0] == "Qwen3ForCausalLM":
             self.llm_config = Qwen3Config(**llm_config)
+        elif llm_config.get("architectures")[0] == "GptOssForCausalLM":
+            self.llm_config = GptOssConfig(**llm_config)
         else:
             raise ValueError(
                 "Unsupported architecture: {}".format(
diff --git a/python/sglang/srt/models/internvl.py b/python/sglang/srt/models/internvl.py
index 925bef445..b146da0e5 100644
--- a/python/sglang/srt/models/internvl.py
+++ b/python/sglang/srt/models/internvl.py
@@ -26,8 +26,10 @@ from sglang.srt.managers.schedule_batch import (
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.deepseek_janus_pro import DropPath
+from sglang.srt.models.gpt_oss import GptOssForCausalLM
 from sglang.srt.models.internlm2 import InternLM2ForCausalLM
 from sglang.srt.models.qwen2 import Qwen2ForCausalLM
+from sglang.srt.models.qwen3 import Qwen3ForCausalLM
 from sglang.srt.models.qwen3_moe import Qwen3MoeForCausalLM
 from sglang.utils import logger
 
@@ -445,6 +447,14 @@ class InternVLChatModel(nn.Module):
             self.language_model = Qwen3MoeForCausalLM(
                 config=config.llm_config, quant_config=quant_config
             )
+        elif config.llm_config.architectures[0] == "GptOssForCausalLM":
+            self.language_model = GptOssForCausalLM(
+                config=config.llm_config, quant_config=quant_config
+            )
+        elif config.llm_config.architectures[0] == "Qwen3ForCausalLM":
+            self.language_model = Qwen3ForCausalLM(
+                config=config.llm_config, quant_config=quant_config
+            )
         else:
             raise NotImplementedError(
                 f"{config.llm_config.architectures[0]} is not implemented."
@@ -577,6 +587,15 @@ class InternVLChatModel(nn.Module):
                 ckpt_up_proj_name="up_proj",
                 num_experts=self.config.num_experts,
             )
+        elif "Qwen3ForCausalLM" in self.config.llm_config.architectures:
+            stacked_params_mapping = [
+                # (param_name, shard_name, shard_id)
+                ("qkv_proj", "q_proj", "q"),
+                ("qkv_proj", "k_proj", "k"),
+                ("qkv_proj", "v_proj", "v"),
+                ("gate_up_proj", "gate_proj", 0),
+                ("gate_up_proj", "up_proj", 1),
+            ]
 
         params_dict = dict(self.named_parameters())
         loaded_params: Set[str] = set()
@@ -661,6 +680,15 @@ class InternVLChatModel(nn.Module):
 
             loaded_params.add(name)
         unloaded_params = params_dict.keys() - loaded_params
+        # Skip params that are created by quantization wrappers and are not expected in the ckpt
+        _quant_only_fragments = (
+            "weight_scale",  # per-matrix FP8 scales (e.g., w2_weight_scale, w13_weight_scale)
+        )
+        unloaded_params = {
+            n
+            for n in unloaded_params
+            if not any(frag in n for frag in _quant_only_fragments)
+        }
         if unloaded_params:
             raise RuntimeError(
                 f"Some weights are not initialized from checkpoints: {unloaded_params}"