NVIDIA Tesla T4
About GPU
The NVIDIA Tesla T4 GPU is a professional-grade graphics processing unit that offers exceptional performance and efficiency for a wide range of compute-intensive workloads. With a base clock speed of 585MHz and a boost clock speed of 1590MHz, the T4 is capable of delivering high levels of processing power for demanding applications.
One of the standout features of the Tesla T4 is its 16GB of GDDR6 memory, which provides ample capacity for storing and manipulating large datasets. The memory clock speed of 1250MHz ensures fast and responsive access to data, while the 2560 shading units enable parallel processing for accelerated performance.
Despite its impressive performance capabilities, the Tesla T4 is also remarkably energy-efficient, with a TDP of just 70W. This means that the T4 can deliver high levels of computational power while keeping power consumption and heat generation to a minimum, making it an attractive option for data center and server deployments.
With a theoretical performance of 8.141 TFLOPS, the Tesla T4 is well-suited for a variety of applications, including deep learning, machine learning, and high-performance computing. Its 4MB of L2 cache further enhances its ability to efficiently handle large and complex workloads.
Overall, the NVIDIA Tesla T4 GPU offers an exceptional combination of performance, efficiency, and versatility, making it a compelling choice for professional users seeking powerful and reliable compute capabilities.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
September 2018
Model Name
Tesla T4
Generation
Tesla
Base Clock
585MHz
Boost Clock
1590MHz
Bus Interface
PCIe 3.0 x16
Memory Specifications
Memory Size
16GB
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
1250MHz
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.
320.0 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.
101.8 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.
254.4 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.
65.13 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.
254.4 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.
8.304
TFLOPS
Miscellaneous
SM Count
?
Multiple Streaming Processors (SPs), along with other resources, form a Streaming Multiprocessor (SM), which is also referred to as a GPU's major core. These additional resources include components such as warp schedulers, registers, and shared memory. The SM can be considered the heart of the GPU, similar to a CPU core, with registers and shared memory being scarce resources within the SM.
40
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.
2560
L1 Cache
64 KB (per SM)
L2 Cache
4MB
TDP
70W
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
3.0
Benchmarks
FP32 (float)
Score
8.304
TFLOPS
Blender
Score
1693
OctaneBench
Score
159
OpenCL
Score
61276
Compared to Other GPU
FP32 (float)
/ TFLOPS
Blender
OctaneBench
OpenCL