AMD Radeon Instinct MI25 Professional Graphics Card

Product status: Unreleased | Report Error

This graphics card has not yet been released, specifications are subject to change without notice.

Original Series
Radeon Instinct
Release Date
1H 2017
Graphics Processing Unit
GPU Model
Vega 10
Vega GCN 5.0
Fabrication Process
14 nm FinFET
Die Size
530 mm2
Stream Processors
Base Clock
Boost Clock
Memory Clock
350 MHz
Effective Memory Clock
1400 Mbps
Memory Configuration
Memory Size
8192 MB
Memory Type
Memory Bus Width
Memory Bandwidth
358.4 GB/s

PCI-Express 3.0 x16
Power Connectors
300 W
Recommended PSU
600 W
API Support
Shader Model

Texture Fillrate
390.7 GTexel/s
SPFP Performance
Performance per W
Performance per mm2
23.6 GFLOPS/mm2

AMD Radeon Instinct MI25
4096 Unified Cores
1526 / 350 MHz 8 GB HBM2
AMD Radeon Instinct MI8
4096 Unified Cores
1000 / 500 MHz 4 GB HBM
AMD Radeon Instinct MI6
2304 Unified Cores
1237 / 2000 MHz 16 GB GDDR5
AMD Radeon Instinct MI25
4096 Cores
1526 / 350 MHz 8 GB HBM2
AMD Radeon Pro 'Vega 10'
4096 Cores
1200 / 250 MHz 16 GB HBM2
AMD Radeon RX Vega
4096 Cores
1550 / 400 MHz 8 GB HBM2

AMD speeds deep learning inference and training with high-performance Radeon Instinct accelerators and MIOpen open-source GPU-accelerated library

SUNNYVALE, CA — (Marketwired) — 12/12/16 — AMD (NASDAQ: AMD) today unveiled its strategy to accelerate the machine intelligence era in server computing through a new suite of hardware and open-source software offerings designed to dramatically increase performance, efficiency, and ease of implementation of deep learning workloads. New Radeon™ Instinct accelerators will offer organizations powerful GPU-based solutions for deep learning inference and training. Along with the new hardware offerings, AMD announced MIOpen, a free, open-source library for GPU accelerators intended to enable high-performance machine intelligence implementations, and new, optimized deep learning frameworks on AMD’s ROCm software to build the foundation of the next evolution of machine intelligence workloads.

Inexpensive high-capacity storage, an abundance of sensor driven data, and the exponential growth of user-generated content are driving exabytes of data globally. Recent advances in machine intelligence algorithms mapped to high-performance GPUs are enabling orders of magnitude acceleration of the processing and understanding of that data, producing insights in near real time. Radeon Instinct is a blueprint for an open software ecosystem for machine intelligence, helping to speed inference insights and algorithm training.

“Radeon Instinct is set to dramatically advance the pace of machine intelligence through an approach built on high-performance GPU accelerators, and free, open-source software in MIOpen and ROCm,” said AMD President and CEO, Dr. Lisa Su. “With the combination of our high-performance compute and graphics capabilities and the strength of our multi-generational roadmap, we are the only company with the GPU and x86 silicon expertise to address the broad needs of the datacenter and help advance the proliferation of machine intelligence.”

At the AMD Technology Summit held last week, customers and partners from 1026 Labs, Inventec, SuperMicro, University of Toronto’s CHIME radio telescope project and Xilinx praised the launch of Radeon Instinct, discussed how they’re making use of AMD’s machine intelligence and deep learning technologies today, and how they can benefit from Radeon Instinct.

Radeon Instinct accelerators feature passive cooling, AMD MultiGPU (MxGPU) hardware virtualization technology conforming with the SR-IOV (Single Root I/O Virtualization) industry standard, and 64-bit PCIe addressing with Large Base Address Register (BAR) support for multi-GPU peer-to-peer support.

Radeon Instinct accelerators are designed to address a wide-range of machine intelligence applications:

  • The Radeon Instinct MI6 accelerator based on the acclaimed Polaris GPU architecture will be a passively cooled inference accelerator optimized for jobs/second/Joule with 5.7 TFLOPS of peak FP16 performance at 150W board power and 16GB of GPU memory
  • The Radeon Instinct MI8 accelerator, harnessing the high-performance, energy-efficient “Fiji” Nano GPU, will be a small form factor HPC and inference accelerator with 8.2 TFLOPS of peak FP16 performance at less than 175W board power and 4GB of High-Bandwidth Memory (HBM)
  • The Radeon Instinct MI25 accelerator will use AMD’s next-generation high-performance Vega GPU architecture and is designed for deep learning training, optimized for time-to-solution

A variety of open source solutions are fueling Radeon Instinct hardware:

  • MIOpen GPU-accelerated library: To help solve high-performance machine intelligence implementations, the free, open-source MIOpen GPU-accelerated library is planned to be available in Q1 2017 to provide GPU-tuned implementations for standard routines such as convolution, pooling, activation functions, normalization and tensor format
  • ROCm deep learning frameworks: The ROCm platform is also now optimized for acceleration of popular deep learning frameworks, including Caffe, Torch 7, and Tensorflow*, allowing programmers to focus on training neural networks rather than low-level performance tuning through ROCm’s rich integrations. ROCm is intended to serve as the foundation of the next evolution of machine intelligence problem sets, with domain-specific compilers for linear algebra and tensors and an open compiler and language runtime

AMD is also investing in developing interconnect technologies that go beyond today’s PCIe Gen3 standards to further performance for tomorrow’s machine intelligence applications. AMD is collaborating on a number of open high-performance I/O standards that support broad ecosystem server CPU architectures including X86, OpenPOWER, and ARM AArch64. AMD is a founding member of CCIX, Gen-Z and OpenCAPI working towards a future 25 Gbit/s phi-enabled accelerator and rack-level interconnects for Radeon Instinct.

Radeon Instinct products are expected to ship in 1H 2017. For more information, visit


* Tensorflow support is expected to be available January 2017.