diff --git a/python/llm/src/ipex_llm/ggml/quantize.py b/python/llm/src/ipex_llm/ggml/quantize.py
index a95e3464e32..f86ee12245f 100644
--- a/python/llm/src/ipex_llm/ggml/quantize.py
+++ b/python/llm/src/ipex_llm/ggml/quantize.py
@@ -53,6 +53,7 @@
                      "sym_int4_rtn": 31,
                      "sym_int8_rtn": 32,
                      "asym_int4_rtn": 33,
+                     "woq_int4": 34,
                      }
 
 # mixed precison from llama.cpp
diff --git a/python/llm/src/ipex_llm/transformers/low_bit_linear.py b/python/llm/src/ipex_llm/transformers/low_bit_linear.py
index 78e10c2c7ac..f78a5168aec 100644
--- a/python/llm/src/ipex_llm/transformers/low_bit_linear.py
+++ b/python/llm/src/ipex_llm/transformers/low_bit_linear.py
@@ -84,6 +84,7 @@
 SYM_INT4_RTN = ggml_tensor_qtype["sym_int4_rtn"]
 SYM_INT8_RTN = ggml_tensor_qtype["sym_int8_rtn"]
 ASYM_INT4_RTN = ggml_tensor_qtype["asym_int4_rtn"]
+WOQ_INT4 = ggml_tensor_qtype["woq_int4"]
 RTN_DTYPE = {
     SYM_INT4_RTN: torch.uint8,
     ASYM_INT4_RTN: torch.uint8,
@@ -187,7 +188,7 @@ def ggml_q_format_convet_cpu2xpu(tensor: torch.Tensor, num_elem: int, qtype: int
     src = ctypes.c_void_p(tensor.data.data_ptr())
 
     if qtype in [SYM_INT4, ASYM_INT4, SYM_INT8, NF4, NF3, FP4, FP6, FP8E4, FP8E5,
-                 Q4_K, Q6_K, FP6_K]:
+                 Q4_K, Q6_K, FP6_K, WOQ_INT4]:
         dst_tensor = torch.empty_like(tensor)
     elif qtype == ggml_tensor_qtype["sym_int5"]:
         QK = ggml.ggml_qk_size(qtype)
@@ -213,7 +214,7 @@ def ggml_q_format_convet_xpu2cpu(tensor: torch.Tensor, num_elem: int, qtype: int
     src = ctypes.c_void_p(tensor.data.data_ptr())
 
     if qtype in [SYM_INT4, ASYM_INT4, SYM_INT8, NF4, NF3, FP4, FP6, FP8E4, FP8E5,
-                 Q4_K, Q6_K, FP6_K]:
+                 Q4_K, Q6_K, FP6_K, WOQ_INT4]:
         dst_tensor = torch.empty_like(tensor)
     elif qtype == ggml_tensor_qtype["sym_int5"]:
         QK = ggml.ggml_qk_size(ggml_tensor_qtype["asym_int5"])
diff --git a/python/llm/src/ipex_llm/transformers/models/utils.py b/python/llm/src/ipex_llm/transformers/models/utils.py
index 3e92146ae3d..9cde0af4056 100644
--- a/python/llm/src/ipex_llm/transformers/models/utils.py
+++ b/python/llm/src/ipex_llm/transformers/models/utils.py
@@ -21,7 +21,7 @@
 from ipex_llm.ggml.quantize import ggml_tensor_qtype
 from ipex_llm.transformers.utils import get_xpu_device_name
 from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8, FP8E5, IQ2_XXS, FP4, FP8E4,\
-    FP6, ASYM_INT4
+    FP6, ASYM_INT4, WOQ_INT4
 
 FP8_KV_ALLOC_LENGTH = 512
 KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
@@ -33,7 +33,7 @@
 
 def decoding_fast_path_qtype_check(proj):
     qtype = getattr(proj, "qtype", None)
-    return qtype in [SYM_INT4, FP8E5, FP4]
+    return qtype in [SYM_INT4, FP8E5, FP4, WOQ_INT4]
 
 
 def init_kv_cache(batch_size, num_heads, head_dim, current_length, max_length, dtype, device):
@@ -248,7 +248,7 @@ def mlp_fusion_check(x, qtype, training):
         return False
     if x.device.type != 'xpu':
         return False
-    if qtype not in [SYM_INT4, FP8E5, FP4, IQ2_XXS, FP6]:
+    if qtype not in [SYM_INT4, FP8E5, FP4, IQ2_XXS, FP6, WOQ_INT4]:
         return False
     if training or x.requires_grad:
         return False
@@ -263,7 +263,7 @@ def use_xmx(x: torch.Tensor, qtype: int):
     device = get_xpu_device_name(x.device)
     return (
         device in ["arc", "pvc"]
-        and qtype in [SYM_INT4, SYM_INT8, FP8E4, FP8E5]
+        and qtype in [SYM_INT4, SYM_INT8, FP8E4, FP8E5, WOQ_INT4]
         and (
             (device == "pvc" and 1 < x.size(0) <= 16)
             or