NVIDIA RTX 3500 Embedded Ada Generation
About GPU
The NVIDIA RTX 3500 Embedded Ada Generation GPU is an impressive piece of hardware, designed specifically for desktops. With a base clock of 1725MHz and a boost clock of 2250MHz, this GPU offers exceptional performance for a wide range of tasks, from gaming to professional workloads.
One of the most notable features of the RTX 3500 is its 12GB of GDDR6 memory, which, combined with a memory clock of 2250MHz, provides ample bandwidth for even the most demanding applications. This makes the GPU particularly well-suited for tasks such as 3D rendering, video editing, and machine learning.
With 5120 shading units and a massive 48MB of L2 cache, the RTX 3500 is capable of handling complex computational tasks with ease. Its TDP of 100W strikes a good balance between performance and power efficiency, making it a viable option for a wide range of desktop systems.
The theoretical performance of 23.501 TFLOPS is nothing short of impressive, and users can expect smooth, fluid experiences in virtually any workload. Whether you're a professional content creator or a hardcore gamer, the RTX 3500 has the power to meet your needs.
In conclusion, the NVIDIA RTX 3500 Embedded Ada Generation GPU offers exceptional performance, robust features, and power efficiency, making it an excellent choice for anyone in need of a high-performance GPU for their desktop system.
Basic
Label Name
NVIDIA
Platform
Desktop
Launch Date
March 2023
Model Name
RTX 3500 Embedded Ada Generation
Generation
Quadro Ada-M
Base Clock
1725MHz
Boost Clock
2250MHz
Bus Interface
PCIe 4.0 x16
Memory Specifications
Memory Size
12GB
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.
192bit
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.
432.0 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.
144.0 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.
360.0 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.
23.04 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.
360.0 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.
23.501
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.
40
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
128 KB (per SM)
L2 Cache
48MB
TDP
100W
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
Benchmarks
FP32 (float)
Score
23.501
TFLOPS
Compared to Other GPU
FP32 (float)
/ TFLOPS