diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 44de7bf391..f0e615b5a1 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -250,7 +250,7 @@ def LlamaAttention_fast_forward_inference(
     sin = self.rotary_emb.sin_cached[seq_len]
     h = head_dim // 2
 
-    RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = Xn.dtype, device = "cuda")
+    RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
     RH_Q[:,:,:,:h] = Qn[:,:,:,h:]; RH_Q[:,:,:,h:] = Qn[:,:,:,:h]; torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h]);
     Qn *= cos; Qn.addcmul_(RH_Q, sin);
 
@@ -259,8 +259,17 @@ def LlamaAttention_fast_forward_inference(
     Kn *= cos; Kn.addcmul_(RH_K, sin);
     
     # New KV cache
-    Kn = torch.cat([K1, Kn], dim = 2)
-    Vn = torch.cat([V1, Vn], dim = 2)
+    # Kn = torch.cat([K1, Kn], dim = 2)
+    # Vn = torch.cat([V1, Vn], dim = 2)
+    if not hasattr(self, "paged_attention_K"):
+        paged_attention = torch.empty((2, bsz, n_kv_heads, 2048, head_dim), dtype = dtype, device = "cuda")
+        self.paged_attention_K = paged_attention[0]
+        self.paged_attention_V = paged_attention[1]
+        self.paged_attention_K[:,:,:kv_seq_len,:] = K1
+        self.paged_attention_V[:,:,:kv_seq_len,:] = V1
+    pass
+    Kn = self.paged_attention_K[:,:,seq_len,:] = K1
+    Vn = self.paged_attention_V[:,:,seq_len,:] = V1
 
     # Grouped query attention
     if n_groups != 1: