How-To Obtain the Latest Official Drivers for an AMD Graphics Product Using the AMD Driver Autodetect Utility

This article provides instructions on how to obtain the latest official drivers for an AMD graphics product using the AMD Driver Autodetect Utility.
NOTE: This tool is designed to provide the latest official AMD Catalyst™ graphics driver for systems running Microsoft Windows. If your system is not running Microsoft Windows or you are looking for an earlier driver or the latest beta driver, you can manually search for a driver from the AMD Graphics Drivers and Software page.

Instructions:

1. Visit the AMD Driver Autodetect Utility page
   
   
   
2. Click on the Download ​link and a web browser prompt should appear.  Select Run from the prompt.
   
   
   
3. Once the download has completed, the AMD Driver Autodetect Utility will launch automatically.​​
4. The AMD Driver Autodetect Utility will analyze the system and show the detected graphics hardware, operating system, and the best driver for the graphics product.
   
   
   
   NOTE:  If the AMD Driver Autodetect Utility is unable to identify the graphics card or operating system, then:
   
   • Manually downloading the drivers from the AMD Graphics Drivers and Software page may be required.  For instructions on how to find the latest drivers for the graphics card from the AMD Graphics Drivers and Software page, refer to KB article: GPU-56
   • A driver may not be available through AMD. For information on graphics products that may not be supported by AMD Catalyst™ drivers, refer to KB article: GPU-125​
5. Click on Download Now to begin downloading the driver.
   
   
   
6. When the driver download process has completed, an installation window will open.  AMD recommends using the default location to eliminate​ potential issues that may occur during installation.
   
   
   
7. Before continuing with the installation process, refer to the following article for detailed installation instructions. The article describes other factors that need to be considered before installing the graphics drivers.    

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpufAAAWWDEFEESQDSWD

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpufAX

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpufS

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf

WHAT IS GPU ACCELERATED COMPUTING?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. Pioneered in 2007 by NVIDIA, GPU accelerators now power energy-efficient datacenters in government labs, universities, enterprises, and small-and-medium businesses around the world. GPUs are accelerating applications in platforms ranging from cars, to mobile phones and tablets, to drones and robots.

HOW GPUS ACCELERATE APPLICATIONS

GPU-accelerated computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user's perspective, applications simply run significantly faster.

 

CPU VERSUS GPU

A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

 

GPUs have thousands of cores to process parallel workloads efficiently

Check out the video clip below for an entertaining CPU versus GPU.

Hundreds of industry-leading applications are already GPU-accelerated.

- See more at: http://www.nvidia.com/object/what-is-gpu-computing.html#sthash.xXahvoQ9.dpuf