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AMD Radeon AI PRO R9700

AMD Radeon AI PRO R9700

AMD Radeon AI PRO R9700: 32 GB for Local AI and Workstations

Radeon AI PRO R9700 is AMD’s professional graphics card built on the RDNA 4 architecture for local inference and AI model development on workstations. It combines 32 GB of GDDR6, 64 compute units (4096 stream processors), and 128 second-generation AI accelerators, supports FP8/FP16/INT8 precisions, connects via PCIe 5.0 x16, and comes in a dual-slot blower design that’s convenient for dense multi-GPU builds. The ROCm stack and popular frameworks (PyTorch, ONNX Runtime, TensorFlow) are supported.

Key Highlights

  • Architecture: RDNA 4, 64 CUs / 4096 SP, 128 second-gen AI accelerators

  • Memory: 32 GB GDDR6, 256-bit bus — headroom for medium and large models (LLMs, multimodal pipelines, generative graphics)

  • AI Performance: up to ~95.7 TFLOPS FP16 and up to 1531 TOPS INT4 (for AIB variants)

  • Interface & Cooling: PCIe 5.0 x16; blower cooler with front-to-back airflow, dual-slot height for multi-card configurations

  • Software & Ecosystem: ROCm 6.4.x, support for PyTorch/ONNX/TensorFlow; Radeon PRO drivers

What It’s Built For

The R9700 targets local inference of medium-to-large LLMs, fine-tuning, and generative pipelines (text-to-image/video, audio), as well as AI-accelerated workflows in CAD/DCC and scientific computing. Here, ample VRAM, stability under sustained load, and multi-GPU scalability are critical.

Why 32 GB of VRAM Matters

Modern LLMs and diffusion models are memory-hungry. With 32 GB, you can keep an entire model (or a large portion of it) fully resident in VRAM, minimizing spills to system RAM or disk. That reduces latency with long prompts, speeds token decoding, and improves pipeline stability for batch inference.

Hardware Platform & Form Factor

The dual-slot blower shroud exhausts hot air out of the chassis, making it easier to build 2–4 GPU systems. A power envelope around ~300 W fits typical professional cases and PSUs, while predictable front-to-back airflow helps maintain thermals during 24/7 workloads.

Software Environment: ROCm and Frameworks

ROCm support makes the card a fit for standard AI stacks: PyTorch, ONNX Runtime, and TensorFlow. For workstations, PRO drivers focus on stability, certification, and reproducibility, alongside profiling and debugging tools. This lowers migration friction from alternative platforms and speeds up time to production.

Positioning in the Lineup

By die and overall characteristics, the R9700 is close to consumer-class counterparts, but it’s tuned for professional AI workloads: expanded VRAM, pro-grade drivers, and a blower design. In tasks where memory capacity and stability outweigh gaming-style clocks, it delivers predictable results and better resource utilization.

Availability & Pricing

Workstation vendors already offer configurations with the R9700; AIB board versions are available at retail. Actual pricing depends on region, taxes, and cooler design, aligning with the typical class of professional adapters featuring 32 GB of VRAM.

Who Should Consider It

  • AI developers and data scientists building local LLM and multimodal pipelines

  • Studios and integrators needing scalable 2–4 GPU workstation builds

  • CAD/DCC engineers and research teams that rely on PRO drivers and long, stable runs

Specifications (at a Glance)

  • GPU: RDNA 4, 64 CUs / 4096 SP, 128 second-gen AI accelerators

  • Memory: 32 GB GDDR6, 256-bit

  • Interface: PCIe 5.0 x16

  • Cooling: Dual-slot blower (front-to-back)

  • Software: ROCm 6.4.x; PyTorch / ONNX Runtime / TensorFlow

  • Peak Metrics (AIB): ~95.7 TFLOPS FP16; up to 1531 TOPS INT4

  • Typical Power Target: ~300 W (reference/ES)

Conclusion

Radeon AI PRO R9700 fills a crucial niche of local AI without memory compromises: 32 GB of VRAM, pro-grade software, and a form factor suited to multi-GPU arrays. It’s a pragmatic choice for teams that need a quiet, predictable, and scalable workstation for LLMs, generative models, and AI-accelerated media pipelines.

Basic

Label Name
AMD
Platform
Desktop
Launch Date
July 2025
Model Name
Radeon AI PRO R9700
Generation
Radeon Pro Navi
Base Clock
1660 MHz
Boost Clock
2920 MHz
Bus Interface
PCIe 5.0 x16
Transistors
53.9 billion
RT Cores
64
Compute Units
64
Tensor Cores
?
Tensor Cores are specialized processing units designed specifically for deep learning, providing higher training and inference performance compared to FP32 training. They enable rapid computations in areas such as computer vision, natural language processing, speech recognition, text-to-speech conversion, and personalized recommendations. The two most notable applications of Tensor Cores are DLSS (Deep Learning Super Sampling) and AI Denoiser for noise reduction.
128
TMUs
?
Texture Mapping Units (TMUs) serve as components of the GPU, which are capable of rotating, scaling, and distorting binary images, and then placing them as textures onto any plane of a given 3D model. This process is called texture mapping.
256
Foundry
TSMC
Process Size
4 nm
Architecture
RDNA 4.0

Memory Specifications

Memory Size
32GB
Memory Type
GDDR6
Memory Bus
?
The memory bus width refers to the number of bits of data that the video memory can transfer within a single clock cycle. The larger the bus width, the greater the amount of data that can be transmitted instantaneously, making it one of the crucial parameters of video memory. The memory bandwidth is calculated as: Memory Bandwidth = Memory Frequency x Memory Bus Width / 8. Therefore, when the memory frequencies are similar, the memory bus width will determine the size of the memory bandwidth.
256bit
Memory Clock
2518 MHz
Bandwidth
?
Memory bandwidth refers to the data transfer rate between the graphics chip and the video memory. It is measured in bytes per second, and the formula to calculate it is: memory bandwidth = working frequency × memory bus width / 8 bits.
644.6GB/s

Theoretical Performance

Pixel Rate
?
Pixel fill rate refers to the number of pixels a graphics processing unit (GPU) can render per second, measured in MPixels/s (million pixels per second) or GPixels/s (billion pixels per second). It is the most commonly used metric to evaluate the pixel processing performance of a graphics card.
373.8 GPixel/s
Texture Rate
?
Texture fill rate refers to the number of texture map elements (texels) that a GPU can map to pixels in a single second.
747.5 GTexel/s
FP16 (half)
?
An important metric for measuring GPU performance is floating-point computing capability. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy.
95.68 TFLOPS
FP64 (double)
?
An important metric for measuring GPU performance is floating-point computing capability. Double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy, while single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.
1495 GFLOPS
FP32 (float)
?
An important metric for measuring GPU performance is floating-point computing capability. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.
48.797 TFLOPS

Miscellaneous

Shading Units
?
The most fundamental processing unit is the Streaming Processor (SP), where specific instructions and tasks are executed. GPUs perform parallel computing, which means multiple SPs work simultaneously to process tasks.
4096
L2 Cache
8 MB
TDP
300W
Vulkan Version
?
Vulkan is a cross-platform graphics and compute API by Khronos Group, offering high performance and low CPU overhead. It lets developers control the GPU directly, reduces rendering overhead, and supports multi-threading and multi-core processors.
1.3
OpenCL Version
2.2
OpenGL
4.6
DirectX
12 Ultimate (12_2)
Power Connectors
1x 16-pin
Shader Model
6.8
ROPs
?
The Raster Operations Pipeline (ROPs) is primarily responsible for handling lighting and reflection calculations in games, as well as managing effects like anti-aliasing (AA), high resolution, smoke, and fire. The more demanding the anti-aliasing and lighting effects in a game, the higher the performance requirements for the ROPs; otherwise, it may result in a sharp drop in frame rate.
128
Suggested PSU
700 W

Benchmarks

FP32 (float)
Score
48.797 TFLOPS

Compared to Other GPU

FP32 (float) / TFLOPS
Quadro NVS 440 PCIe x16
62.546 +28.2%
H100 CNX
52.763 +8.1%
Radeon AI PRO R9700
48.797
GeForce RTX 4070 Ti SUPER AD102
44.982 -7.8%
RTX 4500 Ada Generation
40.423 -17.2%