```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 u = 0.7978845608028654f *
                    fmaf(0.044715f * x, x * x, 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 gelu4(float4 v) {
    v.x = gelu(v.x);
    v.y = gelu(v.y);
    v.z = gelu(v.z);
    v.w = gelu(v.w);
    return v;
}

__device__ __forceinline__ void streaming_store(float4* p, float4 v) {
    asm volatile(
        "st.global.cs.v4.f32 [%0], {%1, %2, %3, %4};"
        :
        : "l"(p), "f"(v.x), "f"(v.y), "f"(v.z), "f"(v.w)
        : "memory"
    );
}

template <typename Index, bool FullTiles>
__global__ __launch_bounds__(256)
void gelu_vector_kernel(
    const float* __restrict__ x,
    float* __restrict__ y,
    Index n
) {
    constexpr int threads = 256;
    constexpr int vectors_per_tile = threads * 4;
    const Index nv = n / 4;
    const float4* __restrict__ xv =
        reinterpret_cast<const float4*>(x);
    float4* __restrict__ yv = reinterpret_cast<float4*>(y);

    const Index tile_stride =
        static_cast<Index>(gridDim.x) * vectors_per_tile;

    for (Index tile = static_cast<Index>(blockIdx.x) * vectors_per_tile;
         tile < nv;
         tile += tile_stride) {
        const Index base = tile + threadIdx.x;
        float4 values[4];

        #pragma unroll
        for (int j = 0; j < 4; ++j) {
            const Index i = base + j * threads;
            if (FullTiles || i < nv)
                values[j] = xv[i];
        }

        #pragma unroll
        for (int j = 0; j < 4; ++j) {
            const Index i = base + j * threads;
            if (FullTiles || i < nv)
                values[j] = gelu4(values[j]);
        }

        #pragma unroll
        for (int j = 0; j < 4; ++j) {
            const Index i = base + j * threads;
            if (FullTiles || i < nv)
                streaming_store(yv + i, values[j]);
        }
    }

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

template <typename Index>
__global__ void gelu_scalar_kernel(
    const float* __restrict__ x,
    float* __restrict__ y,
    Index n
) {
    const Index stride = static_cast<Index>(blockDim.x) * gridDim.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;
    if ((reinterpret_cast<uintptr_t>(x) & 15) == 0) {
        constexpr int elements_per_tile = threads * 16;
        const int blocks = static_cast<int>(
            std::min<int64_t>(
                (n + elements_per_tile - 1) / elements_per_tile, 65535));

        if (n % elements_per_tile == 0) {
            gelu_vector_kernel<Index, true>
                <<<blocks, threads, 0, stream>>>(x, y, static_cast<Index>(n));
        } else {
            gelu_vector_kernel<Index, false>
                <<<blocks, threads, 0, stream>>>(x, y, 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_gelu_streaming_v3",
    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)
```