AMD Radeon Instinct MI300X
About GPU
The AMD Radeon Instinct MI300X GPU is a powerhouse designed for demanding AI and machine learning workloads. With a base clock of 1000MHz and a boost clock of 2100MHz, this GPU offers impressive performance for heavy-duty computing tasks. The large memory size of 192GB, coupled with HBM3 memory type and a memory clock of 2525MHz, ensures that data-intensive operations can be handled with ease.
One of the standout features of the MI300X is its massive 19456 shading units, which contribute to its exceptional processing capabilities. Additionally, the 16MB L2 cache further enhances the GPU's ability to handle complex calculations efficiently.
With a TDP of 750W, the MI300X is a high-power GPU that requires adequate cooling and power supply. However, this significant power consumption is justified by the theoretical performance of 81.72 TFLOPS, making it well-suited for advanced AI training, data analytics, and other compute-intensive tasks.
In terms of real-world performance, the AMD Radeon Instinct MI300X GPU delivers outstanding results, particularly in scenarios where massive parallel processing is required. Its impressive specifications make it a compelling choice for professionals and researchers who need a GPU that can handle the most demanding workloads with ease.
Overall, the AMD Radeon Instinct MI300X GPU stands out as a top-tier option for AI and machine learning applications, offering exceptional processing power and memory capacity to tackle the most challenging computational tasks.
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
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.
0 MPixel/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.
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