Nvidia GPUs with 48 GB Video RAM
Generative AI requires a lot of memory. As a result it is important to understand how much memory a GPU has and if several GPUs can be efficiently connected together to offer more memory. Whether you are using a cloud GPU or building an AI or Deep Learning workstation it is essential to know how much memory is required for your tasks and which GPUs can offer the desired capacity.
Here is the list of NVIDIA GPUs that have 48 GB of video RAM. The list is sorted from the oldest to the newest. Newer GPUs offer more recent features that accelerate deep learning applications, better power efficiency and memory bandwidth. Consequently, the newer GPUs will offer better performance despite all GPUs in the list having the same amount of memory.
Quadro RTX 8000
This GPU with Turing architecture and 48 GB of memory was launched in 2018. The GPU comes equipped with GDDR6 memory, providing lightning-fast speeds with a memory bandwidth of 672 GB/s. It features a 384-bit memory interface, ensuring efficient data transfer between the GPU and memory.
Under the hood, it boasts 4608 CUDA cores for handling parallel computing tasks and 576 second generation Tensor Cores designed for advanced AI and machine learning tasks. The PCI Express 3.0 x16 interface ensures a smooth connection to your system, and the GPU draws 295W of power.
Supporting Compute Compatibility 7.5, Quadro RTX 8000 can handle complex workloads with ease. Additionally, its Tensor Cores are optimized for high-performance computing, supporting data types like FP16, INT1, INT4, and INT8, making it perfect for both AI and deep learning applications.
The GPU comes into two flowers, with active cooling and having video outputs and passive cooling without video outputs. Two NVIDIA Quadro RTX 8000 GPUs can be connected using NVLink to offer 96 GB of memory.
NVIDIA A40
This GPU is built on NVIDIA’s powerful Ampere architecture, released in 2020. It uses GDDR6 memory, delivering impressive performance with a memory bandwidth of 696 GB/s and a 384-bit memory interface for efficient data transfer.
It’s packed with 10,752 CUDA cores for heavy computing tasks and 336 third-generation Tensor Cores, designed to accelerate AI and deep learning operations. The GPU connects through 3 PCIe Gen4 interfaces, and it consumes 300W of power. For cooling, it relies on a passive system.
With compute compatibility of 8.6, NVIDIA A40 is ready for complex workloads. The Tensor Cores support various data types like FP16, INT1, INT4, INT8, TF32, and BF16, making it versatile for different computing needs.
Additionally, NVIDIA NVLink allows you to connect two NVIDIA A40 GPUs, combining them for a total of 96 GB of memory, which can be a game-changer for memory-intensive tasks.
NVIDIA RTX A6000
This GPU is powered by NVIDIA’s advanced Ampere architecture, introduced in 2022. It features GDDR6 memory, delivering fast and reliable performance with a memory bandwidth of 768 GB/s and a 384-bit memory interface to handle demanding tasks with ease.
Packed with 10,752 CUDA cores and 336 third-generation Tensor Cores, NVIDIA RTX A6000 is ideal for handling intensive computational and AI workloads. It connects to your system via a PCI Express Gen 4 x16 interface and draws 300W of power, supported by an active cooling system to ensure it stays cool under pressure.
With a compute compatibility of 8.6, it supports the latest applications, and its Tensor Cores are optimized for FP16, INT1, INT4, INT8, TF32, and BF16 data types, offering flexibility for a variety of computing tasks.
Additionally, NVIDIA NVLink allows you to link two RTX A6000 GPUs, giving you a massive 96 GB of combined memory, perfect for handling large-scale memory-intensive projects.
NVIDIA RTX 6000 Ada Generation
This GPU is built on NVIDIA’s cutting-edge Ada Lovelace architecture, launched in 2022. It uses high-performance GDDR6 memory, with an impressive memory bandwidth of 960 GB/s and a 384-bit memory interface, ensuring smooth and fast data transfer.
With 18,176 CUDA cores and 568 fourth-generation Tensor Cores, NVIDIA RTX 6000 is designed to handle intensive tasks, from gaming to AI and machine learning. It connects to your system through a PCIe 4.0 x16 interface, delivering high-speed connectivity, while consuming 300W of power. The active cooling system ensures it stays cool even during heavy workloads.
It supports compute compatibility 8.9, making it ready for the latest software. The Tensor Cores are optimized for a variety of data types, including FP16, BF16, TF32, INT8, INT4, and even the Hopper FP8 Transformer Engine, making it perfect for AI and deep learning tasks.
However, note that this GPU does not support NVIDIA NVLink.
NVIDIA L20
This GPU is powered by the state-of-the-art Ada Lovelace architecture, introduced in 2023. It features GDDR6 memory with a fast memory bandwidth of 864 GB/s and a 384-bit memory interface, ensuring smooth performance for demanding applications.
With 11,776 CUDA cores and 386 Tensor Cores, NVIDIA L20 is built to handle a wide range of intensive tasks, from high-end gaming to AI and machine learning workloads. It connects to your system through a PCI-Express 4.0 x16 interface, offering fast and reliable data transfer, while drawing 275W of power to keep everything running efficiently.
This setup ensures top-notch performance with a balance of power efficiency and capability.
NVIDIA L40
This GPU is based on the powerful Ada Lovelace architecture, released in 2022. It features high-speed GDDR6 memory with a memory bandwidth of 864 GB/s and a 384-bit memory interface, delivering exceptional data transfer rates for demanding workloads.
With 18,176 CUDA cores and 568 fourth-generation Tensor Cores, NVIDIA L40 is designed for intense computing tasks, making it ideal for AI training, inference, and other high-performance applications. It connects via a PCIe Gen4 x16 interface, offering 64GB/s bi-directional bandwidth, ensuring seamless communication with your system. The GPU draws 300W of power and uses a passive cooling system.
This GPU supports compute compatibility 8.9 and can handle a range of data types, including FP8, FP16, BF16, TF32, INT8, and INT4, giving it the flexibility to excel in various AI and machine learning tasks.
It’s specifically designed for single-GPU training and inference and does not support NVLink.
NVIDIA L40S
This GPU is built on NVIDIA’s advanced Ada Lovelace architecture, launched in 2022. It features GDDR6 memory with a high memory bandwidth of 864 GB/s and a 384-bit memory interface, ensuring efficient and fast data handling for even the most demanding applications.
With 18,176 CUDA cores and 568 fourth-generation Tensor Cores, NVIDIA L40S is optimized for heavy workloads, such as AI training and inference. It connects to your system through a PCIe Gen4 x16 interface, offering a fast and reliable 64GB/s bidirectional bandwidth. Despite its powerful performance, it consumes 350W of power and is cooled using a passive cooling system.
Supporting compute compatibility 8.9, this GPU handles a wide range of data types, including FP8, FP16, BF16, TF32, INT8, and INT4, making it versatile for machine learning and AI tasks.
Designed specifically for single-GPU training and inference, NVIDIA L40S does not support NVLink for multi-GPU configurations.
NVIDIA RTX 5880 Ada Generation
This GPU is powered by NVIDIA’s latest Ada Lovelace architecture, introduced in 2024. It features fast GDDR6 memory with a memory bandwidth of 960 GB/s and a 384-bit memory interface, ensuring smooth and efficient data transfer for high-performance tasks.
With 14,080 CUDA cores and 440 fourth-generation Tensor Cores, NVIDIA RTX 5880 is designed for demanding workloads, such as gaming, AI, and deep learning applications. It connects to your system via a PCIe 4.0 x16 interface, providing fast, reliable communication. Drawing 285W of power, it uses an active cooling system to maintain optimal temperatures during heavy use.
Please note that this GPU does not support NVIDIA NVLink, focusing instead on powerful single-GPU performance.
How to try NVIDIA GPUs with 48 GB of memory
At the time of writing I found the GPUs with 48 GB of memory are available from the following providers.
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If you are interested in building your own AI Deep Learning workstation, I shared my experience in the article below.
References:
- NVIDIA Quadro RTX 8000
- NVIDIA RTX A6000 Graphics Card
- NVIDIA A40 Data Center GPU for Visual Computing
- NVIDIA RTX 5880 Ada Generation Graphics Card
- NVIDIA RTX 6000 Ada Generation Graphics Card
- NVIDIA L20 48GB PCIe GPU Accelerator for HPE
- NVIDIA L40 GPU for Data Center
- L40S GPU for AI and Graphics Performance
- A Database of NVIDIA GPUs
