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NVIDIA RTX 5000 Mobile Ada Embedded

NVIDIA RTX 5000 Mobile Ada Embedded: Power and Innovation in a Compact Form Factor

April 2025

Introduction

The NVIDIA RTX 5000 Mobile Ada Embedded graphics card is a top-tier solution for professionals and gamers who value mobility without compromise. Based on the second-generation Ada Lovelace architecture, it combines cutting-edge technology with optimization for compact systems. In this article, we will explore why this model has become the flagship of 2025 and who it is suitable for.

1. Architecture and Key Features

Ada Lovelace 2.0 Architecture

The card is built on TSMC's 4nm process, offering increased transistor density (up to 22 billion) and energy efficiency. At its core are advanced 4th Generation CUDA Cores, 3.0 RT Cores for ray tracing, and 5.0 Tensor Cores with support for AI algorithms.

Unique Features

- DLSS 4.0: AI scaling up to 8K with minimal loss of detail.

- Ray Tracing Overdrive: A mode for cinematic-quality lighting in games.

- NVIDIA Reflex: Reduces input lag to 15 ms in competitive projects.

- Support for FidelityFX Super Resolution 3.0: Despite being an AMD technology, the card adapts it for hybrid use with DLSS.

2. Memory: Speed and Size

GDDR6X with ECC

Memory capacity is 20 GB with a 320-bit bus and a bandwidth of 960 GB/s. An innovation is the built-in error correction (ECC), which is critical for professional tasks.

Impact on Performance

- 4K Textures: Memory efficiently handles rendering complex scenes in Blender or Unreal Engine 5.3.

- Gaming: In Cyberpunk 2077: Phantom Liberty (2024), at 4K and Ultra settings, VRAM usage does not exceed 16 GB.

3. Gaming Performance

Average FPS in Popular Titles (with DLSS 4.0 activated):

- GTA VI (1440p, Ultra + RT): 85 FPS.

- Starfield: Extended Edition (4K, High): 68 FPS.

- The Witcher 4 (1080p, Ultra + RT Overdrive): 120 FPS.

Ray Tracing

Hardware acceleration of RT cores reduces the FPS drop by 40% compared to software implementation. For instance, in Metro Exodus: Enhanced, at 1440p with RT enabled, the FPS drops only from 90 to 65.

4. Professional Tasks

Video Editing and 3D

- DaVinci Resolve: Renders an 8K project in 12 minutes (compared to 25 minutes for the RTX 4000 Mobile).

- Blender Cycles: CUDA acceleration reduces scene rendering time by 35%.

Scientific Calculations

Support for CUDA 12.5 and OpenCL 3.2 allows the card to be used in neural network simulations (e.g., TensorFlow) and molecular modeling (NAMD).

5. Power Consumption and Heat Generation

TDP and Cooling

- TDP: 175 W (with the ability to reduce to 120 W in power-saving mode).

- Recommendations:

- For laptops — systems with a vapor chamber and at least three fans.

- For embedded solutions (e.g., compact workstations) — active cooling with noise suppression.

Temperatures

Under load: 78–82°C (in well-designed laptops), without throttling.

6. Comparison with Competitors

AMD Radeon RX 7900M XT

- Pros: Cheaper ($2200 versus $2800 for the RTX 5000), higher performance in Vulkan projects.

- Cons: Weaker RT and DLSS performance, no ECC memory.

Intel Arc A9 Mobile

- Price: $1800, but lagging in AI features and support for professional software.

Conclusion: The RTX 5000 Mobile is the choice for those who need a balance between gaming and work.

7. Practical Tips

Power Supply

For laptops: choose models with a PSU of at least 330 W. For embedded platforms — certified 80+ Platinum power sources.

Compatibility

- Supports PCIe 5.0 x16.

- Drivers must be installed using Studio Driver for professional tasks.

Drivers

- Regularly update through GeForce Experience: in 2025, NVIDIA is actively optimizing support for Unreal Engine 6.

8. Pros and Cons

Pros:

- Best-in-class performance with RT and DLSS.

- ECC memory for reliability.

- Support for all current APIs.

Cons:

- Price starting at $2800.

- High heat output in compact cases.

9. Final Conclusion

The RTX 5000 Mobile Ada Embedded is suitable for:

- Professionals: Video editors, 3D artists, engineers needing mobility.

- Gamers: Those who want to play in 4K with maximum quality.

It is an investment in the future: the Ada Lovelace architecture ensures the card's relevance through the end of the 2020s. If the budget allows — it is the best choice on the market.

Prices are current as of April 2025. The listed price is the recommended retail price for new devices.

Basic

Label Name

NVIDIA

Platform

Mobile

Launch Date

March 2023

Model Name

RTX 5000 Mobile Ada Embedded

Generation

Quadro Ada-M

Base Clock

1425MHz

Boost Clock

2115MHz

Bus Interface

PCIe 4.0 x16

Transistors

45,900 million

RT Cores

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.

304

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.

304

Foundry

TSMC

Process Size

5 nm

Architecture

Ada Lovelace

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

2250MHz

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.

576.0 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.

236.9 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.

643.0 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.

41.15 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.

643.0 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.

40.327 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.

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.

9728

L1 Cache

128 KB (per SM)

L2 Cache

64MB

TDP

120W

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 Ultimate (12_2)

CUDA

8.9

Power Connectors

None

Shader Model

6.7

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.

112

Benchmarks

FP32 (float)

Score

40.327 TFLOPS

Compared to Other GPU

FP32 (float) / TFLOPS

Radeon Instinct MI250X

48.827 +21.1%

GeForce RTX 4070 Ti SUPER AD102

44.982 +11.5%

RTX 5000 Mobile Ada Embedded

40.327

Radeon RX 7700 XT

35.873 -11%

GeForce RTX 5080 Mobile

32.115 -20.4%