Yes, because it has many separate kernels instead of aggressive merges like PyTorch (with Torch Compile). Each pass (norm, matmul, residual, RoPE, etc.) launches its own kernel, which increases launch overhead and memory traffic. CuBLAS helps, but it's not enough to compensate.