NVIDIA GPUs with 24 GB of Video RAM
Modern Deep Learning and Generative AI models may require a large amount of GPU memory. When selecting a GPU for your application, you may consider an older GPU with a large amount of memory. In general, newer GPUs offer faster memory, more cache, more CUDA cores, and they support smaller data types such as INT4, INT8, FP8 which increase the throughput for inference workloads. All that results in higher performance and power efficiency for more recent accelerators. NVIDIA GPUs with older architectures such as Maxwell or Pascal do not offer Tensor Cores altogether. Tensor cores were introduced by Volta Architecture and the newer the architecture, the more efficient Tensor Core it provides. On the other side, newer GPUs are more expensive.
Tesla M40 24 GB
Released in 2015, this GPU is powered by NVIDIA’s Maxwell architecture, designed for efficiency and performance. It’s equipped with GDDR5 memory, offering fast data transfer speeds with a bandwidth of 288 GB/s, perfect for demanding tasks like training neural networks, experiments with Generative AI models requiring up to 24 GB of memory, or computational workloads.
The 384-bit memory interface ensures seamless data flow, working hand-in-hand with the 3072 CUDA cores to tackle parallel processing tasks with ease. While the Tesla M40 GPU doesn’t include Tensor Cores, it’s optimized for handling FP32 (single-precision floating point) operations, making it great for applications that rely on precision and speed.
For connectivity, the GPU uses a PCI Express 3.0 x16 system interface, which is still widely compatible with modern systems. It’s designed with a passive cooling system, though it requires 250W of power, so make sure your system can handle it.
Whether you’re upgrading your workstation or building a custom system, this GPU offers a solid balance of performance and efficiency.
Quadro M6000 24 GB
This GPU, released in 2016, runs on NVIDIA’s reliable Maxwell architecture, delivering solid performance for a variety of tasks. It features GDDR5 memory, offering a swift 317 GB/s memory bandwidth to handle data-intensive applications smoothly. With a 384-bit memory interface, data flows efficiently, ensuring quick and responsive performance.
Equipped with 3072 CUDA cores, Quadro M6000 is designed to handle parallel computing tasks with ease. While it doesn’t include Tensor Cores, it’s built to excel in FP32 (single-precision floating point) calculations, making it suitable for workloads requiring precision.
This GPU uses the PCI Express 3.0 x16 interface for high-speed system communication, and with active cooling, it stays cool even under heavy use. It draws 250W of power, so ensure your system’s power supply is up to the task.
Whether you’re gaming, rendering, or tackling computational tasks, this GPU provides a balanced mix of power and performance to meet your needs.
Tesla P40
Released in 2016, this GPU is powered by NVIDIA’s Pascal architecture, a leap forward in both performance and efficiency. It comes with GDDR5 memory, offering an impressive 346 GB/s memory bandwidth to ensure fast data processing. The 384-bit memory interface helps deliver smooth and efficient data flow, even for demanding workloads.
With 3840 CUDA cores, Tesla P40 is built for parallel processing, making it a powerhouse of deep learning inference workloads, and high-performance computing. While it doesn’t include Tensor Cores, it supports both INT8 and FP32 data types, giving it the flexibility to handle a wide range of precision-dependent tasks.
For system connectivity, the GPU utilizes a PCI Express 3.0 x16 interface, ensuring high-speed data transfer. It’s equipped with a passive cooling system, but keep in mind it requires 250W of power, so plan your system’s power supply accordingly.
If you’re looking for a GPU that combines power, flexibility, and efficiency, this Pascal-based model is a solid choice for deep learning applications.
Quadro P6000
Launched in 2016, this GPU is built on NVIDIA’s Pascal architecture, known for its impressive performance and power efficiency. It features GDDR5X memory, a step up from standard GDDR5, delivering a remarkable 432 GB/s memory bandwidth. Paired with a 384-bit memory interface, this ensures lightning-fast data throughput, perfect for high-end applications.
Under the hood, you’ll find 3840 CUDA cores, giving this GPU the muscle to handle complex tasks, from intense gaming to large-scale computational workloads. While it doesn’t come with Tensor Cores, it’s optimized for FP32 data type calculations, making it a strong contender for precision-based tasks.
With a PCI Express 3.0 x16 system interface, Quadro P6000 ensures smooth and fast communication with your system, while its active cooling system keeps the unit running cool under heavy use. Consuming 250W of power, make sure your setup is equipped with a sufficient power supply to keep things running smoothly.
Whether you’re diving into gaming or working on professional-grade rendering, this Pascal-based GPU provides the power, speed, and reliability you need.
Quadro RTX 6000
Released in 2018, this powerhouse GPU is built on NVIDIA’s advanced Turing architecture, designed to deliver next-level performance for professionals and enthusiasts alike. It comes equipped with GDDR6 memory, offering a staggering 672 GB/s memory bandwidth, ensuring blazing-fast data transfers. The 384-bit memory interface further enhances this, allowing smooth operation even with the most demanding applications.
With an impressive 4608 CUDA cores, Quadro RTX 6000 is a beast when it comes to parallel processing. It also features 576 second-generation Tensor Cores, designed to accelerate AI-based tasks and deep learning workloads, making it perfect for training and inference operations. The GPU supports a wide range of data types, including FP16 and FP32, and the Tensor Cores offer support for INT1, INT4, INT8 precision for high-performance inference workloads and FP16 for more complex tasks.
The PCI Express 3.0 x16 system interface ensures fast communication with the system, and the active cooling system helps keep the GPU running at optimal temperatures under heavy loads. With a 295W power requirement, it’s essential to have a robust power supply to keep this GPU performing at its best.
If you need even more power, two of these GPUs can be connected using NVLink, combining their capabilities to offer an incredible 48 GB of memory for the most demanding applications.
Whether you’re working with AI, high-end rendering, or gaming, this Turing-based GPU delivers top-tier performance and flexibility.
TITAN RTX
Introduced in 2018, this GPU is built on NVIDIA’s Turing architecture, designed for exceptional performance across a wide range of demanding tasks. It features high-speed GDDR6 memory, delivering an impressive 672 GB/s memory bandwidth, ensuring quick and efficient data handling. With its 384-bit memory interface, you can expect seamless performance even during the most data-intensive processes.
Boasting 4608 CUDA cores, TITAN RTX is ready for heavy parallel computing tasks, from gaming to professional workloads. It also includes 576 second-generation Tensor Cores, ideal for accelerating AI, machine learning, and deep learning tasks. The GPU supports precision-based data types such as FP16 and FP32, while the Tensor Cores further enhance inference performance with INT1, INT4, INT8, and FP16 precision.
The PCI Express 3.0 x16 interface ensures smooth communication with your system, and the active cooling system keeps the GPU running at optimal temperatures, even under intense workloads. Consuming 280W of power, it’s essential to ensure your system is ready to support this level of performance.
For even greater performance, you can connect two of these GPUs using NVLink, doubling the memory to a massive 48 GB, making it perfect for the most demanding tasks like large-scale simulations or high-performance computing.
Whether you’re focused on AI, deep learning, rendering, or high-end gaming, this Turing-based GPU offers the performance and flexibility you need.
GeForce RTX 3090
Released in 2020, this GPU is built on NVIDIA’s powerful Ampere architecture, delivering high performance for the most demanding tasks. It features GDDR6X memory, offering an astounding 936.2 GB/s of memory bandwidth, ensuring lightning-fast data processing. The 385-bit memory interface allows for efficient and smooth data flow, perfect for handling large workloads.
With an incredible 10,496 CUDA cores, GeForce RTX 3090 excels at parallel processing, making it ideal for everything from gaming to AI and scientific computing. The 328 third-generation Tensor Cores are specifically designed to accelerate machine learning and AI tasks, offering support for precision formats like INT1, INT4, INT8, BF16, FP16, and TF32, making it versatile for both training and inference workloads.
This GPU uses the PCI-Express 4.0 x16 interface, allowing for even faster communication with your system, and features an active cooling system to keep it performing efficiently under heavy use. With a 350W power requirement, it’s a beast, so ensure your system has a robust power supply to support it.
For those who need even more power, you can link two GPUs using NVLink, providing an enormous 48 GB of memory, ideal for large-scale simulations, rendering, or high-performance computing tasks.
Whether you’re working in AI, data science, or gaming, this Ampere-based GPU delivers unparalleled performance, precision, and flexibility.
NVIDIA A10
Unveiled in 2021, this GPU is powered by NVIDIA’s advanced Ampere architecture, offering a blend of performance and efficiency. It comes equipped with GDDR6 memory and delivers a solid 600 GB/s memory bandwidth, making it capable of handling high-speed data transfers with ease. The 384-bit memory interface ensures smooth and efficient performance across a variety of applications.
Packed with 9216 CUDA cores, A10 is designed for parallel processing, making it perfect for a wide range of professional and creative tasks. In addition, it boasts 288 third-generation Tensor Cores, providing exceptional acceleration for AI and machine learning tasks. The support for data types like INT32 and FP32, along with Tensor Core precision for INT1, INT4, INT8, BF16, FP16, and TF32, gives this GPU the versatility to handle both training and inference workloads with precision.
Utilizing the PCI-Express 4.0 x16 interface, this GPU offers fast communication with your system. The passive cooling system ensures silent operation, while the lower 150W power requirement makes it an efficient option for systems where power consumption is a concern.
Whether you’re working on AI projects, machine learning, or high-performance computing, this Ampere-based GPU combines power and efficiency to meet your needs.
NVIDIA A30
Introduced in 2021, this GPU leverages the power of NVIDIA’s Ampere architecture to deliver high-end performance for intensive tasks. Equipped with HBM2 memory, it offers an exceptional 933 GB/s memory bandwidth and a wide 3072-bit memory interface, ensuring fast and efficient data processing, even for the most demanding workloads.
With 3584 CUDA cores, A30 is optimized for parallel computing tasks, making it a strong performer for both professional and creative applications. The 224 third-generation Tensor Cores further enhance its AI capabilities, supporting a wide range of precision formats such as INT1, INT4, INT8, BF16, FP16, TF32, and more, ideal for both training and inference.
This GPU uses the high-speed PCI Express Gen4 interface for seamless communication with your system, and its passive cooling system ensures silent operation while maintaining performance. With a power requirement of 165W, it’s designed to be power-efficient without sacrificing speed.
Additionally, the GPU supports Multi-instance GPU (MiG) technology, allowing it to split into multiple instances for optimized resource allocation. For even more power, NVLink enables two GPUs to be connected, providing enhanced memory and performance scaling.
Whether you’re working in AI, machine learning, or data-intensive tasks, this Ampere-based GPU delivers the performance, precision, and efficiency you need.
RTX A5000
Launched in 2021, this GPU harnesses the power of NVIDIA’s Ampere architecture, delivering top-tier performance for demanding tasks. It features GDDR6 memory with an impressive 768 GB/s memory bandwidth, ensuring fast data transfer speeds. With a 384-bit memory interface, it’s designed to handle large workloads efficiently.
RTX A5000 is packed with 8192 CUDA cores, making it highly capable for parallel processing, while 256 third-generation Tensor Cores boost AI and deep learning performance. These Tensor Cores support a range of precision formats, including INT1, INT4, INT8, BF16, FP16, and TF32, making it highly versatile for AI, machine learning, and inference tasks.
With the latest PCIe 4.0 x16 interface, this GPU ensures fast communication with your system, and its active cooling system keeps it performing at its peak under heavy workloads. The 230W power requirement means it’s powerful, yet efficient enough to be a solid choice for demanding tasks.
For those needing even more power, NVLink allows two GPUs to be connected, offering a combined 48 GB of memory, perfect for large-scale simulations and high-performance computing.
Whether you’re focused on AI, scientific computing, or high-end gaming, this Ampere-based GPU offers a powerful and efficient solution.
GeForce RTX 3090 Ti
Released in 2022, this GPU is built on NVIDIA’s powerful Ampere architecture, designed to deliver unparalleled performance for the most intensive tasks. It features GDDR6X memory, providing an astounding 1.01 TB/s memory bandwidth for ultra-fast data transfer. With a 384-bit memory interface, it handles massive workloads with ease and efficiency.
Boasting an incredible 10,752 CUDA cores, GeForce RTX 3090 Ti is a powerhouse for parallel processing, making it ideal for heavy-duty computing, gaming, and professional applications. The 336 third-generation Tensor Cores further enhance its AI capabilities, supporting a wide range of precision formats including INT1, INT4, INT8, BF16, FP16, and TF32, making it perfect for AI, deep learning, and inference tasks.
The PCI-Express 4.0 x16 interface ensures lightning-fast communication with your system, and the active cooling system keeps the GPU running at optimal temperatures, even under maximum load. With a 450W power requirement, it’s built to deliver high performance while maintaining stability in power-hungry environments.
For those seeking even more power, NVLink allows you to connect two GPUs, offering a combined 48 GB of memory for extreme workloads like large-scale simulations or high-performance computing.
Whether you’re tackling AI research, deep learning, or cutting-edge gaming, this Ampere-based GPU offers a robust and future-proof solution.
RTX A5500
Released in 2022, this GPU is built on NVIDIA’s cutting-edge Ampere architecture, designed to deliver powerful performance for demanding applications. It comes with GDDR6 memory, providing a fast 768 GB/s memory bandwidth, allowing for smooth and efficient data handling. The 384-bit memory interface ensures seamless performance, even with heavy workloads.
Featuring 10,240 CUDA cores, RTX A5500 excels at parallel processing, making it a strong performer for tasks like gaming, rendering, and high-performance computing. The 320 third-generation Tensor Cores further enhance its AI capabilities, offering support for precision formats such as INT1, INT4, INT8, BF16, FP16, and TF32, making it highly versatile for AI and machine learning tasks.
The PCI Express 4.0 x16 interface ensures fast communication with your system, while the active cooling system keeps the GPU running efficiently under high loads. With a 230W power requirement, it delivers strong performance while maintaining energy efficiency.
This Ampere-based GPU is a solid choice for professionals and gamers looking for a balance of power, efficiency, and flexibility.
GeForce RTX 4090
Launched in 2022, this GPU is powered by NVIDIA’s cutting-edge Ada Lovelace architecture, designed to push the boundaries of performance and efficiency. Equipped with GDDR6X memory, it delivers an impressive 1008 GB/s memory bandwidth, ensuring rapid data processing. The 384-bit memory interface ensures smooth and efficient handling of even the most demanding tasks.
With a whopping 16,384 CUDA cores, GeForce RTX 4090 excels at parallel computing, making it ideal for high-end gaming, rendering, and professional workloads. Additionally, it features 512 fourth-generation Tensor Cores, offering advanced AI performance and supporting a wide range of precision formats, including FP8, FP16, BF16, TF32, INT8, and INT4, making it perfect for AI, deep learning, and inference tasks.
The PCI-Express 4.0 x16 interface ensures high-speed communication with your system, and the active cooling system keeps the GPU cool even during intensive use. With a 450W power requirement, this GPU is built to deliver top-tier performance, so make sure your system has the power to match.
Whether you’re focused on AI research, gaming, or high-performance computing, this Ada Lovelace-based GPU offers unparalleled speed, precision, and versatility.
NVIDIA L4
Introduced in 2023, this GPU is built with advanced Ada Lovelace architecture to offer solid performance and efficiency for a variety of tasks. Equipped with GDDR6 memory, it delivers a 300 GB/s memory bandwidth, allowing for smooth data handling across demanding applications. The 192-bit memory interface ensures efficient data flow for seamless performance.
With 7428 CUDA cores, NVIDIA L4 is designed for high-performance parallel computing. It also includes 232 fourth-generation Tensor Cores, which support a wide range of precision formats, including FP8, FP16, BF16, TF32, INT8, and INT4, making it an excellent choice for AI, deep learning, and inference tasks.
This GPU utilizes a PCI Express 4.0 x16 interface for fast communication with your system. It features passive cooling, providing silent operation with a power requirement of only 72W, making it an energy-efficient option for data center environments.
Whether you’re working on AI projects or need reliable performance for Deep Learning tasks, this GPU from the 2023 generation offers a great balance of power and efficiency.
RTX 4500 Ada Generation
Released in 2023, this GPU is powered by NVIDIA’s advanced Ada Lovelace architecture, designed to deliver exceptional performance and efficiency. It features GDDR6 memory with a solid 432 GB/s memory bandwidth, allowing for fast and smooth data transfer. The 192-bit memory interface ensures efficient handling of high workloads, making it ideal for a variety of applications.
With 7680 CUDA cores, RTX 4500 is well-suited for parallel computing, delivering strong performance for gaming, rendering, and other demanding tasks. The 240 fourth-generation Tensor Cores enhance AI capabilities, supporting a range of precision formats like FP8, FP16, BF16, TF32, INT8, and INT4, making this GPU perfect for AI and deep learning applications.
The PCI Express 4.0 x16 interface ensures high-speed system communication, and the active cooling system keeps the GPU running optimally under heavy use. With a power draw of 210W, it strikes a balance between performance and power efficiency.
Whether you’re tackling AI workloads, gaming, or creative tasks, this Ada Lovelace-based GPU offers the speed, precision, and performance you need.
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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 Tesla M40 24 GB
- NVIDIA Quadro M6000 24 GB
- NVIDIA Tesla P40
- NVIDIA Quadro P6000
- NVIDIA Quadro RTX 6000
- NVIDIA TITAN RTX
- NVIDIA GeForce RTX 3090 Family
- NVIDIA NVIDIA AMPERE GA102 GPU ARCHITECTURE
- NVIDIA RTX A5000
- NVIDIA RTX A5000
- NVIDIA A10 Tensor Core GPU
- NVIDIA A30 Tensor Core GPU
- NVIDIA A100 Tensor Core GPU Architecture
- NVIDIA L4 Tensor Core GPU
- NVIDIA ADA GPU ARCHITECTURE
- NVIDIA GeForce RTX 4090
- NVIDIA RTX 4500 Ada Generation
- A searchable database of NVIDIA GPUs
