Advantages
- Higher Boost Clock: 2520MHz (2520MHz vs 1455MHz)
- Larger Memory Size: 48GB (48GB vs 12GB)
- Higher Bandwidth: 1152 GB/s (1152 GB/s vs 651.3 GB/s)
- More Shading Units: 18432 (18432 vs 5120)
Basic
NVIDIA
Label Name
NVIDIA
-
Launch Date
December 2017
Desktop
Platform
Desktop
TITAN Ada
Model Name
TITAN V
GeForce 40
Generation
GeForce 10
2235MHz
Base Clock
1200MHz
2520MHz
Boost Clock
1455MHz
PCIe 4.0 x16
Bus Interface
PCIe 3.0 x16
76,300 million
Transistors
21,100 million
144
RT Cores
-
576
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.
640
576
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.
320
TSMC
Foundry
TSMC
5 nm
Process Size
12 nm
Ada Lovelace
Architecture
Volta
Memory Specifications
48GB
Memory Size
12GB
GDDR6X
Memory Type
HBM2
384bit
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.
3072bit
1500MHz
Memory Clock
848MHz
1152 GB/s
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.
651.3 GB/s
Display and Media
1x HDMI 2.1
3x DisplayPort 1.4a
3x DisplayPort 1.4a
Outputs
1x HDMI 2.0
3x DisplayPort 1.4a
3x DisplayPort 1.4a
Theoretical Performance
483.8 GPixel/s
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.
139.7 GPixel/s
1452 GTexel/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.
465.6 GTexel/s
92.90 TFLOPS
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.
29.80 TFLOPS
1452 GFLOPS
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.
7.450 TFLOPS
91.042
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.
14.602
TFLOPS
Miscellaneous
144
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.
80
18432
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.
5120
128 KB (per SM)
L1 Cache
96 KB (per SM)
96MB
L2 Cache
0MB
800W
TDP
250W
1.3
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
3.0
OpenCL Version
3.0
4.6
OpenGL
4.6
8.9
CUDA
7.0
12 Ultimate (12_2)
DirectX
12 (12_1)
2x 16-pin
Power Connectors
1x 6-pin + 1x 8-pin
192
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.
96
6.7
Shader Model
6.6
1200W
Suggested PSU
600W
Benchmarks
FP32 (float)
/ TFLOPS
TITAN Ada
91.042
+523%
TITAN V
14.602
Share in social media
Or Link To Us
<a href="https://cputronic.com/en/gpu/compare/nvidia-titan-ada-vs-nvidia-titan-v" target="_blank">NVIDIA TITAN Ada vs NVIDIA TITAN V</a>