```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <algorithm>
#include <cstdint>

__device__ __forceinline__ float gelu(float x) {
    const float x2 = x * x;
    const float u = 0.7978845608028654f *
                    fmaf(0.044715f * x, x2, x);
    float t;
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 750
    asm("tanh.approx.f32 %0, %1;" : "=f"(t) : "f"(u));
#else
    t = tanhf(u);
#endif
    return (0.5f * x) * (1.0f + t);
}

__device__ __forceinline__ float4 activate(float4 v) {
    return make_float4(gelu(v.x), gelu(v.y), gelu(v.z), gelu(v.w));
}

template <typename Index, bool FullTiles>
__global__ __launch_bounds__(256)
void gelu_vector_kernel(
    const float4* __restrict__ x,
    float4* __restrict__ y,
    Index n
) {
    constexpr int threads = 256;
    constexpr int tile = 2 * threads;
    const Index vectors = n / 4;
    const Index stride = static_cast<Index>(gridDim.x) * tile;

    for (Index base = static_cast<Index>(blockIdx.x) * tile;
         base < vectors;
         base += stride) {
        const Index i = base + threadIdx.x;
        float4 a, b;

        if (FullTiles || i < vectors)
            a = x[i];
        if (FullTiles || i + threads < vectors)
            b = x[i + threads];

        if (FullTiles || i < vectors)
            a = activate(a);
        if (FullTiles || i + threads < vectors)
            b = activate(b);

        if (FullTiles || i < vectors)
            y[i] = a;
        if (FullTiles || i + threads < vectors)
            y[i + threads] = b;
    }

    if (!FullTiles && blockIdx.x == 0 && threadIdx.x < n % 4) {
        const Index i = vectors * 4 + threadIdx.x;
        reinterpret_cast<float*>(y)[i] =
            gelu(reinterpret_cast<const float*>(x)[i]);
    }
}

template <typename Index>
__global__ __launch_bounds__(256)
void gelu_scalar_kernel(
    const float* __restrict__ x,
    float* __restrict__ y,
    Index n
) {
    const Index stride = static_cast<Index>(gridDim.x) * blockDim.x;
    for (Index i = static_cast<Index>(blockIdx.x) * blockDim.x
                   + threadIdx.x;
         i < n;
         i += stride) {
        y[i] = gelu(x[i]);
    }
}

template <typename Index>
void launch_gelu(
    const float* x,
    float* y,
    int64_t n,
    cudaStream_t stream
) {
    constexpr int threads = 256;
    constexpr int tile_elements = threads * 8;

    if ((reinterpret_cast<uintptr_t>(x) & 15) == 0) {
        const int blocks = static_cast<int>(
            std::min<int64_t>((n + tile_elements - 1) / tile_elements,
                              4096));

        const float4* xv = reinterpret_cast<const float4*>(x);
        float4* yv = reinterpret_cast<float4*>(y);

        if (n % tile_elements == 0) {
            gelu_vector_kernel<Index, true>
                <<<blocks, threads, 0, stream>>>(
                    xv, yv, static_cast<Index>(n));
        } else {
            gelu_vector_kernel<Index, false>
                <<<blocks, threads, 0, stream>>>(
                    xv, yv, static_cast<Index>(n));
        }
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((n + threads - 1) / threads, 4096));
        gelu_scalar_kernel<Index><<<blocks, threads, 0, stream>>>(
            x, y, static_cast<Index>(n));
    }
}

torch::Tensor gelu_cuda(torch::Tensor input) {
    TORCH_CHECK(input.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(input.scalar_type() == at::kFloat, "Expected float32");

    const c10::cuda::CUDAGuard guard(input.device());
    auto x = input.contiguous();
    auto y = torch::empty_like(x);
    const int64_t n = x.numel();

    if (n == 0)
        return y;

    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

    if (n < (int64_t(1) << 30)) {
        launch_gelu<int>(
            x.data_ptr<float>(), y.data_ptr<float>(), n, stream);
    } else {
        launch_gelu<int64_t>(
            x.data_ptr<float>(), y.data_ptr<float>(), n, stream);
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return y;
}
"""

_gelu_extension = load_inline(
    name="fused_vectorized_gelu_v6",
    cpp_sources="torch::Tensor gelu_cuda(torch::Tensor input);",
    cuda_sources=_cuda_source,
    functions=["gelu_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3", "--use_fast_math"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return _gelu_extension.gelu_cuda(x)
```