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NVIDIA RTX 4000 SFF Ada Generation

NVIDIA RTX 4000 SFF Ada Generation: Compact Power for Professionals and Gamers

April 2025

1. Architecture and Key Features: Ada Lovelace in Miniature

The NVIDIA RTX 4000 SFF Ada Generation graphics card is built on the Ada Lovelace architecture, which represents an evolutionary step after Ampere. The chips are manufactured using TSMC's 4nm process, ensuring higher transistor density and energy efficiency.

Key Features:

- DLSS 3.5 with improved AI upscaling and frame generation. The technology now works even in older games thanks to universal algorithms.

- Third-generation RT cores for ray tracing: 50% faster than in the RTX 3000 series.

- FP8 Tensor cores accelerate machine learning tasks.

- Support for AV1 for video encoding/decoding—crucial for streamers and editors.

Despite its compact form factor (SFF - Small Form Factor), the card retains all the key features of the “larger” models, including NVIDIA Reflex for reducing latency in games.

2. Memory: Speed and Capacity for Multitasking

The RTX 4000 SFF is equipped with 16GB GDDR6X on a 256-bit bus, with a bandwidth of 768 GB/s. This is 20% higher than the previous-generation RTX 4000.

How does this affect performance?

- In games at 4K, the memory size allows avoiding stuttering on Ultra texture settings.

- For professionals: rendering complex 3D scenes in Blender without buffer overload.

- NVLink is absent, but for SFF devices, this is justified—a focus on compactness.

3. Gaming Performance: 4K Without Compromises

The card is optimized for 1440p and 4K resolutions. Examples of FPS (with DLSS 3.5 in Quality mode):

- Cyberpunk 2077: Phantom Liberty (with RT Ultra): 68 FPS (4K).

- Starfield: Odyssey (modifications with ray tracing): 75 FPS (1440p).

- Apex Legends (without RT): 144 FPS (4K).

Ray tracing reduces FPS by 25-30%, but DLSS 3.5 compensates for the losses. Enabling RT is justified even in SFF builds thanks to effective cooling.

4. Professional Tasks: Not Just Gaming

- Video Editing: 8K rendering in DaVinci Resolve is 30% faster than with the RTX A4500.

- 3D Modeling: In Autodesk Maya, CUDA cores accelerate rendering by 40% compared to the previous generation.

- Scientific Calculations: Support for CUDA 12.5 and OpenCL 3.0 makes the card suitable for simulations in MATLAB and ANSYS.

Tip: For workstations, choose NVIDIA Studio drivers—they are optimized for professional software.

5. Power Consumption and Heat Dissipation: A Quiet Compact Beast

- TDP: 150W — lower than the “full-size” RTX 4070 (220W).

- Cooling: Two-slot cooler with a pair of fans. Even under load, noise does not exceed 32 dB.

Case Recommendations:

- Mini-PCs in ITX format with ventilation on the side panel.

- Ideal options: Fractal Design Terra, Cooler Master NR200.

6. Comparison with Competitors: Who is in the Lead?

- AMD Radeon Pro W7600SFF: 12GB GDDR6, worse in ray tracing, but cheaper ($899).

- Intel Arc A770S: 16GB GDDR6, excellent price ($699), but weak support for professional applications.

RTX 4000 SFF wins in balancing gaming and professional performance, but the price is higher—$1299.

7. Practical Tips: Building the System Correctly

- Power Supply: At least 500W (recommended 650W for headroom).

- Compatibility: PCIe 5.0, but works on 4.0 with minimal losses.

- Drivers: For hybrid tasks (gaming + work), use Game Ready Driver with manual setting selection.

8. Pros and Cons

Pros:

- Compact without sacrificing 4K performance.

- Support for all current NVIDIA technologies.

- Quiet operation even under load.

Cons:

- High price ($1299).

- No NVLink for scalability.

9. Final Conclusion: Who is This Card For?

RTX 4000 SFF Ada Generation is the ideal choice for:

- Professionals who need a mobile workstation (editing, 3D).

- Gamers building PCs in compact cases without compromising on 4K.

- SFF enthusiasts who value a balance of power and design.

If the budget is limited, consider AMD or Intel, but for top performance in a small form factor, there are currently no alternatives to NVIDIA.

Prices are current as of April 2025. Check availability with NVIDIA's official partners.

Basic

Label Name

NVIDIA

Platform

Professional

Launch Date

March 2023

Model Name

RTX 4000 SFF Ada Generation

Generation

Quadro Ada

Base Clock

720MHz

Boost Clock

1560MHz

Bus Interface

PCIe 4.0 x16

Transistors

35,800 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.

192

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.

192

Foundry

TSMC

Process Size

5 nm

Architecture

Ada Lovelace

Memory Specifications

Memory Size

20GB

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.

160bit

Memory Clock

1750MHz

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.

280.0 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.

124.8 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.

299.5 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.

19.17 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.

299.5 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.

18.787 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.

6144

L1 Cache

128 KB (per SM)

L2 Cache

48MB

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

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.

Suggested PSU

250W

Benchmarks

FP32 (float)

Score

18.787 TFLOPS

Blender

Score

4561

Vulkan

Score

105965

OpenCL

Score

122596

Compared to Other GPU

FP32 (float) / TFLOPS

Radeon Pro V620

20.686 +10.1%

Radeon RX 9070 XT

19.512 +3.9%

RTX 4000 SFF Ada Generation

18.787

RTX A5000 Max-Q

16.922 -9.9%

Tesla V100 SXM2 16 GB

16.023 -14.7%

Blender

GeForce RTX 5090

15026.3 +229.5%

RTX 4000 SFF Ada Generation

4561

GeForce RTX 2070

2020.49 -55.7%

Radeon RX 6800S

1064 -76.7%

GeForce GTX 980 Ti

552 -87.9%

Vulkan

GeForce RTX 5090 D

382809 +261.3%

RTX A5000

140875 +32.9%

RTX 4000 SFF Ada Generation

105965

Radeon RX 5700

61331 -42.1%

T1000

34688 -67.3%

OpenCL

GeForce RTX 5090 D

385013 +214.1%

A10G

167342 +36.5%

RTX 4000 SFF Ada Generation

122596

GeForce RTX 2060

75816 -38.2%

CMP 30HX

57474 -53.1%