diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index 86a4c258..6b577c29 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -1055,23 +1055,47 @@ def _optimize_post(model, lightweight_bmm=False):
                             module.SelfAttention,
                             chatglm_attention_forward
                             )
-        elif (model.config.num_layers == 40 and hasattr(model.config, 'rope_ratio')
-                and model.config.rope_ratio == 500):
-            # glm-4-9b-chat
+        elif model.config.num_layers == 40 and hasattr(model.config, 'rope_ratio'):
             modeling_module_name = model.__class__.__module__
             module = importlib.import_module(modeling_module_name)
-            from ipex_llm.transformers.models.chatglm4 import chatglm4_attention_forward
-            from ipex_llm.transformers.models.chatglm4 import chatglm4_model_forward
-            from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
-            convert_forward(model,
-                            module.SelfAttention,
-                            chatglm4_attention_forward)
-            convert_forward(model,
-                            module.ChatGLMModel,
-                            chatglm4_model_forward)
-            convert_forward(model,
-                            module.RMSNorm,
-                            chatglm_rms_norm_forward)
+            if hasattr(model.transformer, "vision"):
+                # glm-4v-9b
+                modeling_module_name = model.transformer.vision.__class__.__module__
+                vision_module = importlib.import_module(modeling_module_name)
+                from ipex_llm.transformers.models.chatglm4v import chatglm4v_attention_forward
+                from ipex_llm.transformers.models.chatglm4v import chatglm4v_model_forward
+                from ipex_llm.transformers.models.chatglm4v import visual_attention_forward
+                from ipex_llm.transformers.models.chatglm4v import patch_embedding_forward
+                from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
+                convert_forward(model,
+                                module.SelfAttention,
+                                chatglm4v_attention_forward)
+                convert_forward(model,
+                                module.ChatGLMModel,
+                                chatglm4v_model_forward)
+                convert_forward(model,
+                                module.RMSNorm,
+                                chatglm_rms_norm_forward)
+                convert_forward(model,
+                                vision_module.Attention,
+                                visual_attention_forward)
+                convert_forward(model,
+                                vision_module.PatchEmbedding,
+                                patch_embedding_forward)
+            else:
+                # glm-4-9b-chat
+                from ipex_llm.transformers.models.chatglm4 import chatglm4_attention_forward
+                from ipex_llm.transformers.models.chatglm4 import chatglm4_model_forward
+                from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
+                convert_forward(model,
+                                module.SelfAttention,
+                                chatglm4_attention_forward)
+                convert_forward(model,
+                                module.ChatGLMModel,
+                                chatglm4_model_forward)
+                convert_forward(model,
+                                module.RMSNorm,
+                                chatglm_rms_norm_forward)
 
     elif "mpt" in model.config.model_type:
         if model.config.architectures is not None:
diff --git a/python/llm/src/ipex_llm/transformers/models/chatglm4v.py b/python/llm/src/ipex_llm/transformers/models/chatglm4v.py
new file mode 100644
index 00000000..6ddb2509
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/models/chatglm4v.py
@@ -0,0 +1,340 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# 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.
+#
+# This file is adapted from
+# https://huggingface.co/THUDM/glm-4v-9b/blob/main/modeling_chatglm.py
+#
+
+import torch
+from typing import Optional, Tuple, Union
+from ipex_llm.transformers.models.utils import restore_fp8_kv_cache, update_past_key_value
+from ipex_llm.transformers.models.utils import use_quantize_kv_cache, use_sdp, use_sdp_causal
+from ipex_llm.transformers.models.utils import should_use_fuse_rope, apply_rotary_pos_emb
+from ipex_llm.transformers.models.chatglm2 import repeat_kv
+from ipex_llm.utils.common import invalidInputError
+from transformers.modeling_outputs import BaseModelOutputWithPast
+import math
+from typing import List
+
+
+# copied from https://huggingface.co/THUDM/glm-4v-9b/blob/main/modeling_chatglm.py#L753
+def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
+    if images_list is None or len(images_list) == 0:
+        return True
+    for image_list in images_list:
+        if image_list is not None:
+            return False
+    return True
+
+
+def chatglm4v_model_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    images: torch.Tensor = None,
+    position_ids: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.BoolTensor] = None,
+    full_attention_mask: Optional[torch.BoolTensor] = None,
+    past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
+    inputs_embeds: Optional[torch.Tensor] = None,
+    use_cache: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    # generate mode with past_key_values. the image features are already mapped
+    if past_key_values is None:
+        # not allow for inputs_embeds, because we want to process image feature
+        invalidInputError(input_ids is not None and inputs_embeds is None,
+                          f"{input_ids} should not be None, {inputs_embeds} should be None.")
+        if not is_empty(images):  # multi-modality
+            image_size: int = self.config.vision_config['image_size']
+            patch_size: int = self.config.vision_config['patch_size']
+            num_patches = (image_size // patch_size // 2) ** 2
+            invalidInputError(len(input_ids) == len(images),
+                              f"{len(input_ids)} should equal to {len(images)}")
+            inputs_embeds = self.embedding(input_ids)
+
+            images = images.to(dtype=inputs_embeds.dtype)
+            images_features = self.vision(images)
+
+            if position_ids is None:
+                position_ids = self.get_position_ids(input_ids, device=inputs_embeds.device)
+            new_input_embeds, new_position_ids = [], []
+
+            for i in range(len(input_ids)):
+                input_id = input_ids[i].tolist()
+                boi_token_pos = input_id.index(self.config.boi_token_id)
+                eoi_token_pos = input_id.index(self.config.eoi_token_id)
+                invalidInputError(eoi_token_pos - boi_token_pos == 2,
+                                  "eoi_token_pos - boi_token_pos should equal to 2, but got"
+                                  f"{eoi_token_pos} - {boi_token_pos} = "
+                                  f"{eoi_token_pos - boi_token_pos}")
+                new_input_embeds.append(torch.cat(
+                    (inputs_embeds[i, :boi_token_pos],
+                     images_features[i].to(inputs_embeds.device),
+                     inputs_embeds[i, eoi_token_pos + 1:])))
+                new_position_ids.append(torch.cat(
+                    (position_ids[i, :boi_token_pos + 1],
+                     position_ids[i, boi_token_pos + 1].repeat(num_patches),
+                     position_ids[i, eoi_token_pos:])
+                ))
+            inputs_embeds = torch.stack(new_input_embeds, dim=0)
+            position_ids = torch.stack(new_position_ids, dim=0)
+
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else
+        self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    batch_size, seq_length = input_ids.shape
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embedding(input_ids)
+
+    if full_attention_mask is None:
+        if (attention_mask is not None and not attention_mask.all()) or\
+                (past_key_values and seq_length != 1):
+            full_attention_mask = self.get_masks(input_ids,
+                                                 past_key_values,
+                                                 padding_mask=attention_mask)
+
+    # ipex-llm changes begin
+    # 1. replace `rotary_pos_emb` with `inv_freq` and `position_ids`
+    # 2. generate `causal_mask` and replace `full_attention_mask` with it
+    if position_ids is None:
+        if past_key_values is None:
+            position_ids = torch.arange(seq_length, dtype=torch.int64, device=inputs_embeds.device)
+        else:
+            kv_length = past_key_values[0][0].size(2)
+            position_ids = torch.arange(kv_length, kv_length + seq_length,
+                                        dtype=torch.int64, device=inputs_embeds.device)
+        position_ids = position_ids.repeat(batch_size, 1)
+
+    if not getattr(self.rotary_pos_emb, "cached", False):
+        rot_dim = self.rotary_pos_emb.dim
+        base = 10000 * getattr(self.rotary_pos_emb, "rope_ratio", 1)
+        # We should generate float inv_freq to avoid overflow, as base is too large.
+        inv_freq = 1.0 / (base ** (torch.arange(0, rot_dim, 2,
+                                                dtype=torch.float,
+                                                device=inputs_embeds.device) / rot_dim))
+        inv_freq = inv_freq.to(inputs_embeds.dtype)
+        self.rotary_pos_emb.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.rotary_pos_emb.cached = True
+
+    # `full_attention_mask` is not None only when
+    #  `past_key_values` is not None and `seq_length` > 1
+    if full_attention_mask is not None:
+        causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
+                                  dtype=inputs_embeds.dtype, device=inputs_embeds.device)
+        mask_value = torch.finfo(inputs_embeds.dtype).min
+        causal_mask.masked_fill_(full_attention_mask, mask_value)
+    elif self.training or (inputs_embeds.device.type != "xpu" and past_key_values is None):
+        full_attention_mask = self.get_masks(input_ids,
+                                             past_key_values,
+                                             padding_mask=attention_mask)
+        causal_mask = torch.zeros([batch_size, 1, seq_length, full_attention_mask.size(-1)],
+                                  dtype=inputs_embeds.dtype, device=inputs_embeds.device)
+        mask_value = torch.finfo(inputs_embeds.dtype).min
+        causal_mask.masked_fill_(full_attention_mask, mask_value)
+    else:
+        causal_mask = None
+
+    hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
+        inputs_embeds, causal_mask,
+        rotary_pos_emb=(self.rotary_pos_emb.inv_freq, position_ids),
+        kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
+    )
+    # ipex-llm changes end
+
+    if presents is not None and type(presents) is torch.Tensor:
+        presents = presents.split(1, dim=0)
+        presents = list(presents)
+        presents = [list(x.squeeze(0).split(1, dim=0)) for x in presents]
+        presents = [tuple([x.squeeze(0) for x in y]) for y in presents]
+        presents = tuple(presents)
+
+    if not return_dict:
+        return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
+                     if v is not None)
+
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=presents,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attentions,
+    )
+
+
+def chatglm4v_attention_forward(
+    self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
+):
+    # hidden_states: [b, sq, h]
+    bsz, q_len, _ = hidden_states.size()
+
+    # past_key_value: [bsz, n_kv_head, seq_len, head_dim]
+    past_key_value = None
+    if kv_cache is not None:
+        if isinstance(kv_cache, tuple):
+            past_key_value = kv_cache
+        else:
+            past_key_value = (kv_cache[0][0], kv_cache[0][1])
+
+    n_head = self.num_attention_heads_per_partition
+    n_kv_head = self.num_multi_query_groups_per_partition if self.multi_query_attention else n_head
+    head_dim = self.hidden_size_per_attention_head
+
+    qkv = self.query_key_value(hidden_states)
+    # [bs, q_len, np * 3 * hn] -> [bsz, n_head, seq_len, head_dim]
+    qkv = qkv.view(bsz, q_len, n_head + 2 * n_kv_head, head_dim)
+    qkv = qkv.transpose(1, 2)
+
+    query_states, key_states, value_states = qkv.split([n_head,
+                                                        n_kv_head,
+                                                        n_kv_head], dim=1)
+
+    kv_seq_len = key_states.shape[2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[2]
+
+    # IPEX-LLM OPT: fuse rope
+    inv_freq, position_ids = rotary_pos_emb
+    rot_dim = inv_freq.size(-1) * 2
+    if should_use_fuse_rope(hidden_states, rotary_pos_emb[1], self.training):
+        import xe_addons
+        xe_addons.rotary_two_inplaced(inv_freq, position_ids,
+                                      query_states[..., :rot_dim], key_states[..., :rot_dim])
+    else:
+        idx_theta = torch.outer(position_ids[0].float(),
+                                inv_freq.float()).to(hidden_states.dtype)
+        idx_theta = idx_theta.unsqueeze(0).unsqueeze(0)
+        cos = torch.cos(idx_theta).repeat_interleave(2, -1)
+        sin = torch.sin(idx_theta).repeat_interleave(2, -1)
+        q_rot, k_rot = apply_rotary_pos_emb(query_states[..., :rot_dim], key_states[..., :rot_dim],
+                                            cos, sin, position_ids, "chatglm")
+        query_states[..., :rot_dim] = q_rot[...]
+        key_states[..., :rot_dim] = k_rot[...]
+
+    # IPEX-LLM OPT: kv cache and quantize kv
+    use_quantize_kv = use_quantize_kv_cache(self.query_key_value, query_states)
+    key_states, value_states = update_past_key_value(
+        past_key_value, key_states, value_states,
+        kv_seq_len, use_quantize_kv, hidden_states.device
+    )
+
+    if use_cache:
+        if past_key_value is None:
+            past_key_value = torch.cat((key_states.unsqueeze(0).unsqueeze(0),
+                                        value_states.unsqueeze(0).unsqueeze(0)), dim=1)
+        else:
+            past_key_value = (key_states, value_states)
+    else:
+        past_key_value = None
+
+    # IPEX-LLM OPT: sdp
+    attn_weights = None
+    if use_sdp(q_len, kv_seq_len, head_dim, query_states):
+        import xe_addons
+        if use_quantize_kv:
+            attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask)
+        else:
+            attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask)
+    elif use_sdp_causal(q_len, kv_seq_len, head_dim, query_states, self.training):
+        import xe_addons
+        if use_quantize_kv:
+            attn_output = xe_addons.sdp_fp8_causal(query_states, key_states, value_states,
+                                                   attention_mask)
+        else:
+            attn_output = xe_addons.sdp_causal(query_states, key_states, value_states,
+                                               attention_mask)
+    elif query_states.device.type == "cpu":
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, n_head // n_kv_head)
+        value_states = repeat_kv(value_states, n_head // n_kv_head)
+        if q_len == kv_seq_len:
+            attn_output = torch.nn.functional.scaled_dot_product_attention(
+                query_states, key_states, value_states, is_causal=True
+            )
+        else:
+            attn_output = torch.nn.functional.scaled_dot_product_attention(
+                query_states, key_states, value_states, attention_mask
+            )
+    else:
+        if use_quantize_kv:
+            key_states, value_states = restore_fp8_kv_cache(key_states, value_states,
+                                                            query_states.dtype)
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, n_head // n_kv_head)
+        value_states = repeat_kv(value_states, n_head // n_kv_head)
+        attn_weights = torch.matmul(query_states / math.sqrt(head_dim),
+                                    key_states.transpose(2, 3)).to(value_states.dtype)
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+        if kv_seq_len >= 2048 or bsz >= 64:
+            # for memory considerations, do not upcast attention to fp32
+            # for long sequences or large batches
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
+        else:
+            # upcast attention to fp32
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
+                                                       dtype=torch.float32).to(value_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+    # context_layer's shape: [bsz, n_head, seq_len, head_dim] -> [seq_len, bsz, n_head * head_dim]
+    attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, n_head * head_dim)
+    output = self.dense(attn_output)
+
+    return output, past_key_value
+
+
+def visual_attention_forward(self, x: "tensor(B, L, D)") -> "tensor(B, L, D)":
+    B, L, _ = x.shape
+    qkv = self.query_key_value(x)
+    qkv = qkv.reshape(B, L, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)  # 3, B, H, L, D
+    q, k, v = qkv[0], qkv[1], qkv[2]
+
+    bsz, q_len, kv_seq_len, head_dim = q.shape
+    if use_sdp(q_len, kv_seq_len, head_dim, q):
+        import xe_addons
+        out = xe_addons.sdp(q, k, v, None)
+    elif q.device.type == "cpu":
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v,
+                                                               attn_mask=None,
+                                                               dropout_p=0.,
+                                                               is_causal=False)
+    else:
+        attn_weights = torch.matmul(q / math.sqrt(head_dim),
+                                    k.transpose(2, 3)).to(v.dtype)
+        if kv_seq_len >= 2048 or bsz >= 64:
+            # for memory considerations, do not upcast attention to fp32
+            # for long sequences or large batches
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
+        else:
+            # upcast attention to fp32
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
+                                                       dtype=torch.float32).to(v.dtype)
+        out = torch.matmul(attn_weights, v)
+    output = self.dense(out.transpose(1, 2).reshape(B, L, -1))
+    output = self.output_dropout(output)
+    return output
+
+
+def patch_embedding_forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
+    x = self.proj(images)
+    x = x.flatten(2).transpose(1, 2)
+    cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
+    x = torch.cat((cls_token, x), dim=1)
+    x += self.position_embedding.weight.unsqueeze(0).to(images.device)
+    return x