NVIDIA RTX TITAN Ada
The NVIDIA RTX TITAN Ada GPU is an absolute powerhouse of a graphics card. With a boosted clock speed of 2520MHz and an enormous 48GB of GDDR6X memory, this GPU is designed to handle even the most demanding workloads with ease. The 18432 shading units and 96MB of L2 cache ensure that no detail is overlooked, and the 800W TDP ensures that the TITAN Ada has all the power it needs to perform at its best.
The sheer theoretical performance of 92.9 TFLOPS is truly impressive, and it's clear that the TITAN Ada is designed for professionals who need the absolute best performance money can buy. Whether you're working on complex 3D rendering, machine learning, or advanced simulations, the TITAN Ada has the muscle to handle it all.
The TITAN Ada's high memory capacity and bandwidth make it a top choice for deep learning and AI research, where large datasets and complex models demand a significant amount of memory and processing power. For professionals in these fields, the TITAN Ada is a game-changer.
In conclusion, the NVIDIA RTX TITAN Ada GPU is a true marvel of engineering, offering unparalleled performance and capabilities for the most demanding tasks. While it comes with a hefty price tag, for professionals who need the absolute best, the TITAN Ada is well worth the investment.
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Basic
Label Name
NVIDIA
Platform
Desktop
Launch Date
January 2023
Model Name
RTX TITAN Ada
Generation
GeForce 40
Base Clock
2235MHz
Boost Clock
2520MHz
Bus Interface
PCIe 4.0 x16
Memory Specifications
Memory Size
48GB
Memory Type
GDDR6X
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.
384bit
Memory Clock
1500MHz
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.
1152 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.
483.8 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.
1452 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.
92.90 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.
1452 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.
96.653
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.
144
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.
18432
L1 Cache
128 KB (per SM)
L2 Cache
96MB
TDP
800W
Benchmarks
FP32 (float)
Score
96.653
TFLOPS
Compared to Other GPU
FP32 (float)
/ TFLOPS