NVIDIA RTX A500 Embedded
 
                                    
                                    The NVIDIA RTX A500 Embedded GPU is a powerful and efficient option for professional use. With a base clock of 1192MHz and a boost clock of 1627MHz, this GPU can handle demanding tasks with ease. Its 4GB of GDDR6 memory and 1750MHz memory clock ensure quick data access and smooth performance.
The RTX A500 features 2048 shading units and 2MB of L2 cache, allowing for complex calculations and rendering. Its TDP of 35W makes it suitable for embedded systems where power consumption is a concern. Despite its lower power usage, the GPU still offers impressive theoretical performance, delivering 6.664 TFLOPS of computational power.
One of the standout features of the NVIDIA RTX A500 is its support for real-time ray tracing and AI-enhanced workflows, thanks to its RT and Tensor Cores. This capability makes it an excellent choice for professionals working in industries such as engineering, design, and content creation.
Overall, the NVIDIA RTX A500 Embedded GPU is a reliable and high-performance option for professionals who require a balance of power and efficiency. Its support for advanced technologies, combined with its impressive specs, makes it a compelling choice for embedded systems and applications where reliable GPU performance is crucial. Whether used for medical imaging, edge AI, or other professional applications, the RTX A500 delivers the performance and features needed to handle demanding workloads.
                                
                                
                                                    Basic
                            Label Name
                        
                        
                            NVIDIA
                        
                    
                                Platform
                            
                            
                                Professional
                            
                        Model Name
                                                    
                            
                                RTX A500 Embedded
                                                            
                        
                    Generation
                                                    
                            
                                Quadro Mobile
                                                            
                        
                    Base Clock
                                                    
                            
                                1192MHz
                                                            
                        
                    Boost Clock
                                                    
                            
                                1627MHz
                                                            
                        
                    Bus Interface
                                                    
                            
                                PCIe 4.0 x16
                                                            
                        
                    Transistors
                                                    
                            
                                Unknown
                                                            
                        
                    RT Cores
                                                    
                            
                                16
                                                            
                        
                    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.
                                
                            
                                64
                                                            
                        
                    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.
                                
                            
                                64
                                                            
                        
                    Foundry
                                                    
                            
                                Samsung
                                                            
                        
                    Process Size
                                                    
                            
                                8 nm
                                                            
                        
                    Architecture
                                                    
                            
                                Ampere
                                                            
                        
                    Memory Specifications
Memory Size
                                                    
                            
                                4GB
                                                            
                        
                    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.
                                
                            
                                64bit
                                                            
                        
                    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.
                                
                            
                                112.0 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.
                                
                            
                                78.10 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.
                                
                            
                                104.1 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.
                                
                            
                                6.664 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.
                                
                            
                                104.1 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.
                                
                            
                                6.531
                                                                    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.
                                
                            
                                16
                                                            
                        
                    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.
                                
                            
                                2048
                                                            
                        
                    L1 Cache
                                                    
                            
                                128 KB (per SM)
                                                            
                        
                    L2 Cache
                                                    
                            
                                2MB
                                                            
                        
                    TDP
                                                    
                            
                                35W
                                                            
                        
                    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
                                                    
                            
                                8.6
                                                            
                        
                    Power Connectors
                                                    
                            
                                None
                                                            
                        
                    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.
                                
                            
                                48
                                                            
                        
                    Benchmarks
                                                FP32 (float)
                                            
                                            
                                                                                                Score
                                            
                                            
                                                6.531
                                                TFLOPS
                                            
                                            Compared to Other GPU
                                    FP32 (float)
                                                                             / TFLOPS
                                                                    
                                                                    
                                                                    
                                                                    
                                                                    
                                                                    
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