NVIDIA introduces new low-end Ampere hardware with ray tracing and tensor core support, the RTX A1000 and A400, as replacements for the T1000/T600/T400 lineup, offering consistent graphics features and a half-height half-length board design with active cooling.
- Introduction of two new low-end Ampere hardware cards
- First entry-level desktop proviz cards from NVIDIA to feature ray tracing and tensor core support
- Consistent set of graphics features across NVIDIA's proviz lineup
nVidia is bidding farewell to its Turing architecture as it retires several of its video card products. The company is now focusing on its low-end Ampere hardware, introducing two new desktop cards to its professional visualization lineup.
Replacing the T1000/T600/T400 lineup, which was released in 2021, the new RTX A1000 and RTX A400 cards are part of the entry-level category. These cards complete the RTX A series of proviz cards, offering NVIDIA’s Ampere-generation professional graphics technologies in a low-power, low-performance, and low-cost configuration.
What sets these new cards apart is that they are the first entry-level desktop proviz cards from NVIDIA to feature Ray Tracing and tensor core support. As a result, they are now being promoted as RTX-branded video cards, eliminating the overlap with NVIDIA’s compute cards.
While the ray tracing performance of these cards may not be , they provide a consistent set of graphics features across NVIDIA’s proviz lineup.
The RTX A1000 and A400 share the same board design, with NVIDIA opting for no physical feature differentiation this time around. Both cards are based on the GA107 GPU but have different core and memory configurations.
The RTX A1000 features 2304 CUDA cores and 72 Tensor Cores, paired with 8GB of GDDR6 memory running at 12Gbps. It offers a total memory bandwidth of 192GB/second and has a TDP of 50 Watts.
On the other hand, the RTX A400 is more cut down, with about a third of the active hardware on the GPU and half the memory bandwidth compared to the A1000. This results in approximately 40% of the performance of the T1000 and a memory bandwidth of 96GB/second. Despite the hardware reduction, the TDP remains at 50 Watts.
Both cards feature a half-height half-length (HHHL) board design with active cooling. They are single-slot cooler designs and come with four Mini DisplayPorts, offering DP 1.4a functionality.
It’s worth noting that the A1000 and A400 have slightly different video capabilities. The A1000 has access to both of GA107’s NVDEC video decode blocks, while the A400 only has access to a single block.
The RTX A1000 is available now through NVIDIA’s distribution partners, while the RTX A400 will be available in May. OEMs are expected to offer these cards as part of their pre-built systems this summer.
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Background Information
About nVidia:
NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.
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Technology Explained
GDDR6: GDDR6 stands for Graphics Double Data Rate 6th generation memory. It is a high performance memory used in graphics cards and graphics processing units (GPUs), specifically targeting gaming, AI and deep learning-related applications. GDDR6 achieves higher bandwidth than previous generations, allowing faster and smoother gaming experience for users. It is also more power efficient, resulting in lower energy consumption overall. The improved power efficiency makes it adaptable to today's needs of thinner laptops and ultra-high definition gaming laptops. Additionally, GDDR6 is used in storage solutions and advanced data center applications to help streamline large amounts of data at lightning-fast speeds.
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GPU: GPU stands for Graphics Processing Unit and is a specialized type of processor designed to handle graphics-intensive tasks. It is used in the computer industry to render images, videos, and 3D graphics. GPUs are used in gaming consoles, PCs, and mobile devices to provide a smooth and immersive gaming experience. They are also used in the medical field to create 3D models of organs and tissues, and in the automotive industry to create virtual prototypes of cars. GPUs are also used in the field of artificial intelligence to process large amounts of data and create complex models. GPUs are becoming increasingly important in the computer industry as they are able to process large amounts of data quickly and efficiently.
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Ray Tracing: Ray tracing is an advanced rendering technique used in computer graphics to simulate the way light interacts with objects in a virtual environment. It traces the path of light rays as they travel through a scene, calculating how they interact with surfaces, materials, and lighting sources. By simulating the complex behaviors of light, ray tracing produces highly realistic and accurate visual effects, including lifelike reflections, shadows, and refractions. This technology enhances the overall visual quality of images and animations by accurately replicating how light behaves in the real world, resulting in a more immersive and visually stunning digital experience.
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Tensor Cores: Tensor Cores are a type of specialized hardware designed to accelerate deep learning and AI applications. They are used in the computer industry to speed up the training of deep learning models and to enable faster inference. Tensor Cores are capable of performing matrix operations at a much faster rate than traditional CPUs, allowing for faster training and inference of deep learning models. This technology is used in a variety of applications, including image recognition, natural language processing, and autonomous driving. Tensor Cores are also used in the gaming industry to improve the performance of games and to enable more realistic graphics.
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