diff --git a/comfy/ldm/kandinsky5/model.py b/comfy/ldm/kandinsky5/model.py
index a8cf894e4..393b69e34 100644
--- a/comfy/ldm/kandinsky5/model.py
+++ b/comfy/ldm/kandinsky5/model.py
@@ -148,9 +148,23 @@ class FeedForward(nn.Module):
         self.in_layer = operations.Linear(dim, ff_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.activation = nn.GELU()
         self.out_layer = operations.Linear(ff_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.num_chunks = 4
 
     def forward(self, x):
-        return self.out_layer(self.activation(self.in_layer(x)))
+        #return self.out_layer(self.activation(self.in_layer(x)))
+        # ffn is the peak memory consumer, chunking here helps
+        B, L, C = x.shape
+        chunk_size = (L + self.num_chunks - 1) // self.num_chunks
+        output = torch.empty(B, L, self.out_layer.out_features, dtype=x.dtype, device=x.device)
+        
+        for i in range(0, L, chunk_size):
+            end_idx = min(i + chunk_size, L)
+            def compute_chunk(x_chunk):
+                activated = self.activation(self.in_layer(x_chunk))
+                return self.out_layer(activated)
+            output[:, i:end_idx] = compute_chunk(x[:, i:end_idx])
+        
+        return output
 
 
 class OutLayer(nn.Module):