Home / NVIDIA / NVIDIA TITAN V: Performance and Specs

NVIDIA TITAN V

NVIDIA TITAN V

NVIDIA TITAN V: A Legend of Computation in the Era of New Technologies

April 2025

Introduction

The NVIDIA TITAN V, released in 2017, has been a revolution for professionals and enthusiasts alike. Despite its age, this graphics card continues to attract interest due to its unique architecture. However, in 2025, its role has changed. In this article, we explore who still finds TITAN V relevant today and how it handles modern tasks.

Architecture and Key Features

Volta: The Foundation of Power

The TITAN V is built on the Volta architecture, serving as a bridge between gaming and professional solutions. The manufacturing process is 12 nm from TSMC, which may seem outdated by 2025 standards but was groundbreaking at the time.

Tensor Cores: AI Acceleration

The standout feature is its 5120 CUDA cores and 640 Tensor Cores (a first in consumer GPUs). These accelerate machine learning and scientific computing tasks. However, it lacks support for RTX (ray tracing) and DLSS, as these technologies were introduced in later architectures like Turing and Ampere.

Absence of FidelityFX

FidelityFX is an AMD technology for image enhancement, which is not utilized in NVIDIA products. Instead, the TITAN V relies on raw computational power.

Memory: Speed vs. Capacity

HBM2: Elite Standard

The card is equipped with 12 GB of HBM2 memory with a bandwidth of 653 GB/s. In comparison, even modern GDDR6X (like in the RTX 4080) offers around 600–700 GB/s but falls short in efficiency.

Impact on Performance

HBM2 enables lightning-fast data processing in rendering tasks and neural networks. However, for gaming in 4K, 12 GB may be insufficient—newer titles like Starfield 2 or GTA VI Remastered demand 16+ GB.

Gaming Performance: Nostalgia or Relevance?

FPS in Popular Games

- Cyberpunk 2077: Phantom Liberty (Ultra, 1440p): ~45 FPS (without ray tracing).

- Call of Duty: Black Ops V (Ultra, 4K): ~35 FPS.

- Fortnite (Epic, 1080p): ~120 FPS.

The TITAN V still handles games at high settings in 1080p and 1440p but struggles in 4K due to memory limitations and lack of DLSS.

Ray Tracing: The Weak Link

Without hardware support for RTX, enabling ray tracing in Alan Wake 3 or The Elder Scrolls VI drops the FPS to 15–20, which is unacceptable.

Professional Tasks: Where TITAN V Still Shines

3D Rendering and Editing

In Blender and Cinema 4D, the card delivers performance close to the RTX 3090 thanks to its CUDA cores. For instance, rendering a scene in Blender Cycles takes 12 minutes compared to 10 minutes on the RTX 4090.

Scientific Computing and AI

Tensor Cores make the TITAN V ideal for training small neural networks. In tests with ResNet-50, it even outperforms the RTX 3060.

Software Support

Optimization for CUDA and OpenCL remains a strong point. However, for new APIs like HIP (AMD's alternative to CUDA), the card is less effective.

Power Consumption and Heat Generation

TDP: 250 W

The power consumption is comparable to the RTX 4080 (320 W), but efficiency is lower. A power supply of 600 W is recommended for stable operation.

Cooling and Case

A case with good ventilation (for example, the Fractal Design Meshify 2) and a minimum of three fans is advised. Noise under load can reach up to 42 dB, which is higher than modern counterparts with liquid cooling.

Comparison with Competitors

NVIDIA RTX 4090

- Pros of RTX 4090: DLSS 3.5, 24 GB GDDR6X, RTX support.

- Pros of TITAN V: Superior performance in specific computations (such as FP64).

AMD Radeon RX 7900 XTX

- Cheaper (~$999 compared to $2999 for the TITAN V), but weaker in tasks involving Tensor Cores.

Who Should Clearly Choose What

The TITAN V is relevant for labs and AI developers who require precision in calculations. Gamers would be better off choosing the RTX 4070 Ti or newer.

Practical Advice

Power Supply

A minimum of 600 W with an 80+ Gold certification (for instance, Corsair RM650x).

Compatibility

- PCIe 3.0 x16 slot (backward compatibility with PCIe 4.0/5.0).

- Drivers: Use Studio Drivers for professional tasks, but updates for gaming stopped in 2023.

Pros and Cons

Pros

- Unmatched performance in FP64 computations.

- HBM2 memory for fast professional tasks.

- Legendary status and reliability.

Cons

- Price: New units still cost around ~$2500–$3000.

- No support for RTX/DLSS.

- High power consumption.

Final Conclusion: Who Should Consider TITAN V in 2025?

This graphics card is a specialized tool. It's ideal for:

- Scientists and engineers working with precise calculations.

- Machine learning enthusiasts on a budget.

- Collectors and hardware fans.

For gamers and most professionals (like video editors), it's better to opt for modern RTX 40-series or Radeon RX 7000 graphics cards. The TITAN V remains a niche solution, reminiscent of how NVIDIA began the revolution in AI acceleration.

Prices are valid as of April 2025. These pertain to new devices.

Read more...

Basic

Label Name
NVIDIA
Platform
Desktop
Launch Date
December 2017
Model Name
TITAN V
Generation
GeForce 10
Base Clock
1200MHz
Boost Clock
1455MHz
Bus Interface
PCIe 3.0 x16
Transistors
21,100 million
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
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
Foundry
TSMC
Process Size
12 nm
Architecture
Volta

Memory Specifications

Memory Size
12GB
Memory Type
HBM2
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
Memory Clock
848MHz
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

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.
139.7 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.
465.6 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.
29.80 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.
7.450 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

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
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
L1 Cache
96 KB (per SM)
L2 Cache
0MB
TDP
250W
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
OpenGL
4.6
DirectX
12 (12_1)
CUDA
7.0
Power Connectors
1x 6-pin + 1x 8-pin
Shader Model
6.6
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
Suggested PSU
600W

Benchmarks

FP32 (float)
Score
14.602 TFLOPS
3DMark Time Spy
Score
12960
Blender
Score
1803.73
Vulkan
Score
144316
OpenCL
Score
146970

Compared to Other GPU

FP32 (float) / TFLOPS
RTX 3000 Mobile Ada Generation
15.932 +9.1%
Tesla PG503 216
15.357 +5.2%
TITAN V
14.602
RTX A4500 Max-Q
14.024 -4%
Tesla V100 FHHL
13.474 -7.7%
3DMark Time Spy
GeForce RTX 4090
36233 +179.6%
Radeon RX 6800
16792 +29.6%
TITAN V
12960
GeForce RTX 2070
9097 -29.8%
GeForce RTX 2070 SUPER Max Q
7333 -43.4%
Blender
GeForce RTX 4070 Ti
7429 +311.9%
GeForce RTX 3080 Mobile
3235 +79.4%
TITAN V
1803.73
Radeon RX 5700
966.13 -46.4%
Quadro M6000 24 GB
492 -72.7%
Vulkan
GeForce RTX 5090 D
382809 +165.3%
TITAN V
144316
Radeon RX 7600
91662 -36.5%
Radeon RX 5700
61331 -57.5%
T1000
34688 -76%
OpenCL
GeForce RTX 5090 D
385013 +162%
A10G
167342 +13.9%
TITAN V
146970
GeForce RTX 2060
75816 -48.4%
CMP 30HX
57474 -60.9%