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NVIDIA CUDA Toolkit

July, 9th 2025 - 3.3 GB - Freeware

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Latest Version

NVIDIA CUDA Toolkit 12.9.1 (for Windows 11) LATEST
Review by

Daniel Leblanc
Operating System

Windows 11
User Rating

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Author / Product

NVIDIA Corporation / External Link
Filename

cuda_12.9.1_576.57_windows.exe

NVIDIA CUDA Toolkit provides a development environment for creating high-performance GPU-accelerated applications.

With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and HPC supercomputers.

The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to deploy your application.

GPU-accelerated CUDA libraries enable drop-in acceleration across multiple domains such as linear algebra, image and video processing, deep learning, and graph analytics. For developing custom algorithms, you can use available integrations with commonly used languages and numerical packages as well as well-published development APIs.

Your CUDA applications can be deployed across all NVIDIA GPU families available on-premise and on GPU instances in the cloud. Using built-in capabilities for distributing computations across multi-GPU configurations, scientists and researchers can develop applications that scale from single GPU workstations to cloud installations with thousands of GPUs.

IDE with graphical and command-line tools for debugging, identifying performance bottlenecks on the GPU and CPU, and providing context-sensitive optimization guidance. Develop applications using a programming language you already know, including C, C++, Fortran, and Python.

To get started, browse through online getting started resources, optimization guides, illustrative examples, and collaborate with the rapidly growing developer community. Download NVIDIA CUDA Toolkit for PC today!

Features and Highlights

GPU Timestamp: Start timestamp
Method: GPU method name. This is either "memcpy*" for memory copies or the name of a GPU kernel. Memory copies have a suffix that describes the type of a memory transfer, e.g. "memcpyDToHasync" means an asynchronous transfer from Device memory to Host memory
GPU Time: It is the execution time for the method on GPU
CPU Time: It is the sum of GPU time and CPU overhead to launch that Method. At driver generated data level, CPU Time is only CPU overhead to launch the Method for non-blocking Methods; for blocking methods it is a sum of GPU time and CPU overhead. All kernel launches by default are non-blocking. But if any profiler counters are enabled kernel launches are blocking. Asynchronous memory copy requests in different streams are non-blocking
Stream Id: Identification number for the stream
Columns only for kernel methods
Occupancy: Occupancy is the ratio of the number of active warps per multiprocessor to the maximum number of active warps
Profiler counters: Refer the profiler counters section for a list of counters supported
grid size: Number of blocks in the grid along the X, Y, and Z dimensions are shown as [num_blocks_X num_blocks_Y num_blocks_Z] in a single column
block size: Number of threads in a block along X, Y, and Z dimensions is shown as [num_threads_X num_threads_Y num_threads_Z]] in a single column
dyn smem per block: Dynamic shared memory size per block in bytes
sta smem per block: Static shared memory size per block in bytes
reg per thread: Number of registers per thread
Columns only for memcopy methods
mem transfer size: Memory transfer size in bytes
host mem transfer type: Specifies whether a memory transfer uses "Pageable" or "Page-locked" memory

PROS

Massive Parallel Processing Power
Optimized for NVIDIA GPUs
Strong Developer Support
Wide AI & HPC Applications
Seamless Integration with Libraries

CONS

Limited to NVIDIA GPUs
Steep Learning Curve
High Power Consumption
Hardware Upgrade Costs
Not Ideal for All Workloads

Also Available: Download NVIDIA CUDA Toolkit for Mac

Download NVIDIA CUDA Toolkit Latest Version

Why is this app published on FileHorse? (More info)

What's new in this version:

General CUDA:
CUDA Toolkit Major Components:
- Starting with CUDA 11, individual components within the CUDA Toolkit (for example: compiler, libraries, tools) are versioned independently

New Features:
CUDA Compiler:
CUDA Developer Tools:
- For changes to nvprof and Visual Profiler, see the changelog
- For new features, improvements, and bug fixes in Nsight Systems, see the changelog
- For new features, improvements, and bug fixes in Nsight Visual Studio Edition, see the changelog
- For new features, improvements, and bug fixes in CUPTI, see the changelog
- For new features, improvements, and bug fixes in Nsight Compute, see the changelog
- For new features, improvements, and bug fixes in Compute Sanitizer, see the changelog
- For new features, improvements, and bug fixes in CUDA-GDB, see the changelog

Fixed:
CUDA Compiler:
- Starting with CUDA 12.8, we observed miscompilation issues caused by incorrect code generation for address calculations involving large immediate values (i.e., values that exceed the bounds of a 32-bit integer). This miscompiled code can lead to runtime errors such as “illegal memory access” on SM90 and SM100. The issue has been resolved in CUDA 12.9.1.
- The problem can be triggered by a PTX pattern in which a group of add instructions sharing the same base operand but use different immediate values as the second operand. These immediate values exceed the bounds of a 32-bit integer. The register values used in the add instructions are all warp-uniform, and an add instruction with the larger immediate value is scheduled before the one with the smaller immediate value.