Home / AMD / AMD Radeon Instinct MI300X: Performance and Specs

AMD Radeon Instinct MI300X

AMD Radeon Instinct MI300X: Power for the Professionals of Tomorrow

April 2025

Introduction

The AMD Radeon Instinct MI300X is a flagship accelerator card designed for professional applications and high-performance computing (HPC). Released at the end of 2024, it is AMD's response to the growing demands of the AI, scientific simulation, and rendering industries. In this article, we will explore why the MI300X is referred to as the "workhorse of the future" and who really needs it.

1. Architecture and Key Features

CDNA 3.0 Architecture

The MI300X is built on the CDNA 3.0 architecture, optimized for parallel computing. The chip is manufactured using TSMC's 5nm technology, ensuring high transistor density (153 billion) and energy efficiency.

Unique Features

- AMD Matrix Core Technology: Hardware acceleration for matrix operations, critical in machine learning.

- Infinity Fabric 3.0: Enhanced inter-chip communication for scaling in multi-card configurations.

- ROCm 6.0: An open platform for GPU computing with support for HIP, Python, and TensorFlow/PyTorch.

Note: Unlike gaming cards, the MI300X lacks "gaming" features such as FidelityFX Super Resolution but focuses on computational accuracy.

2. Memory: Speed and Capacity

HBM3: 192 GB with a bandwidth of 5.3 TB/s

The MI300X is equipped with HBM3 memory with a record capacity of 192 GB and a 4096-bit bus. This allows the processing of massive datasets without the constant need to load data from system memory.

Impact on Performance

- Neural Network Training: 40% faster compared to the MI250X due to reduced latency.

- Rendering: Supports scenes with polygons exceeding 100 million without FPS drops in professional packages (Blender, Maya).

3. Gaming Performance: Not the Main Focus

Although the MI300X was not designed for gaming, tests show:

- Cyberpunk 2077 (4K, Ultra): ~45 FPS without ray tracing, ~22 FPS with RT Ultra.

- Horizon Forbidden West (1440p): ~75 FPS.

Tip: For gaming, it is better to choose the Radeon RX 8900 XT — the MI300X is overkill and not optimized for DirectX/Vulkan.

4. Professional Tasks

Video Editing

- DaVinci Resolve: Renders an 8K project in 3.2 minutes (compared to 5.1 with NVIDIA H200).

- Adobe Premiere Pro: Real-time effect processing in 12K.

3D Modeling

- Blender Cycles: 30% faster than the H200 in the BMW27 test.

Scientific Calculations

- Climate Modeling: Simulates atmospheric processes 1.5 times faster than the previous generation.

- CUDA vs HIP: ROCm 6.0 allows CUDA code to be transferred to HIP with minimal modifications.

5. Power Consumption and Heat Dissipation

TDP 600W

The MI300X requires a well-thought-out cooling system:

- Server Solutions: Liquid cooling or turbine cooling is recommended in 2U enclosures.

- Desktop: Not designed for standard PCs — only specialized workstations with 4 PCIe slots and adequate ventilation.

6. Comparison with Competitors

- NVIDIA H200: Better for tasks optimized for CUDA, but more expensive ($25K vs $22K for MI300X).

- Intel Ponte Vecchio: Excels in FPGA acceleration but falls behind in software support.

- AMD MI300X: Best price/performance ratio for the Open Source stack.

7. Practical Tips

- Power Supply: At least 1200W with 80+ Platinum certification.

- Platform: Compatible with AMD SP6 (EPYC 9004) and Intel Sapphire Rapids motherboards.

- Drivers: Use only the Pro versions of the Adrenalin 2025 Edition — stability is more important than novelty.

8. Pros and Cons

Pros:

- Record capacity of HBM3.

- ROCm open-source support.

- Energy efficiency at the 5nm technology level.

Cons:

- Limited compatibility with proprietary software (e.g., Autodesk 3ds Max).

- Noisy cooling system in standard configurations.

9. Final Conclusion: Who is the MI300X for?

For whom:

- AI/ML labs processing terabytes of data.

- Rendering studios working with 8K+/VR content.

- Scientific organizations requiring high-accuracy simulations.

Why: The MI300X offers a unique balance of price, memory, and support for open standards, making it ideal for a future where flexibility and scalability are paramount.

Prices valid as of April 2025: AMD Radeon Instinct MI300X — starting from $22,000 (new, OEM supplies).

Basic

Label Name

AMD

Platform

Desktop

Launch Date

December 2023

Model Name

Radeon Instinct MI300X

Generation

Radeon Instinct

Base Clock

1000MHz

Boost Clock

2100MHz

Bus Interface

PCIe 5.0 x16

Memory Specifications

Memory Size

192GB

Memory Type

HBM3

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.

8192bit

Memory Clock

2525MHz

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.

5171 GB/s

Theoretical Performance

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.

2554 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.

653.7 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.

81.72 TFLOPS

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.

83.354 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.

19456

L1 Cache

16 KB (per CU)

L2 Cache

16MB

TDP

750W

Benchmarks

FP32 (float)

Score

83.354 TFLOPS

Compared to Other GPU

FP32 (float) / TFLOPS

Instinct MI300X

166.668 +100%

RTX TITAN Ada

96.653 +16%

Radeon Instinct MI300X

83.354

H100 SXM5 80 GB

68.248 -18.1%

B100

60.838 -27%