GPU Programming
Accelerate breakthroughs in AI and high-performance computing, unlocking the limitless potential of massive high-speed GPU computing. From CUDA kernel development to multi-GPU parallel computing, NexGPU provides native GPU power.
1#include <stdio.h>23__global__ void vector_add(float *A, float *B, float *C, int N) {4 int idx = threadIdx.x + blockIdx.x * blockDim.x;5 if (idx < N) {6 C[idx] = A[idx] + B[idx];7 }8}910int main() {11 const int N = 1024;12 size_t size = N * sizeof(float);1314 float *h_A = (float *)malloc(size);15 float *h_B = (float *)malloc(size);16 float *h_C = (float *)malloc(size);
Purpose-Built for GPU Development
Native GPU Computing
Access native GPU power for custom CUDA-based application development. Supports CUDA C/C++, OpenCL, Vulkan Compute and other GPU programming interfaces.
Architecture-Level Optimization
Optimize for specific architectures like A100, H100, or RTX 4090 to boost performance. Fully leverage Tensor Cores, RT Cores and other hardware acceleration units.
Full Admin Access
Use full admin privileges to configure drivers, memory, and execution environments. Freely install any CUDA version, compilers, and debugging tools.
Rapid Test & Iterate
Test and iterate across multiple GPU types with minimal configuration. Quickly validate code compatibility and performance across different architectures.
Related Guides
Get Started: GPU Programming Templates
Use pre-built templates to quickly launch your GPU development environment.
NVIDIA CUDA
Base Docker image designed as the starting point for all containerized GPU development. Pre-installed with CUDA Toolkit, cuDNN, and NCCL, ready to use.
Unlock the Limitless Potential of GPU Computing
Whether it's research algorithm validation, CUDA kernel optimization, or HPC application development, NexGPU provides flexible, cost-effective, high-performance native GPU power.