NVIDIA GPUs with 16 GB of VRAM

7 min readOct 27, 2024

NVIDIA GPUs with 16 GB o video RAM for AI and Deep Learning — Photo by Andrey Matveev on Unsplash

If you would like to start learning AI and Deep Learning, the best way to begin is to use free online options like Google Colab or Kaggle Notebooks. The free version of Google Colab offers a 16 GB T4 NVIDIA GPU which is optimized for inference workloads. While you can definitely use T4 for training neural networks, optimized for inference means that it can accelerate the computation involving some smaller data types that are important for inference. Also, some older datacenter GPUs such as Tesla P100 and Tesla P100 with 16 GB of video RAM are still offered by cloud providers such as Google Cloud.

If you have a limited budget, you can select the GPU that has enough memory to fit your models and compromise on GPU performance. By that I mean that newer GPUs usually have more CUDA cores, which is important for computation parallelization, but are more expensive. In addition, newer GPU models also have Tensor Cores that are important for performance and newer GPUs have better Tensor Cores. Last but not least, newer GPUs can work with smaller precision data types that make inference faster as was mentioned before.

Tesla P100 for PCIe 16 GB

This NVIDIA GPU, based on the Pascal architecture, was released in 2016 and is designed for high-performance computing with a focus on data-intensive tasks. It uses HBM2 memory technology, delivering an impressive 732 GB/s memory bandwidth across a 4096-bit memory interface. With 3,584 CUDA cores, this GPU is built for substantial parallel processing, though it doesn’t include tensor cores.

The GPU connects via PCI-Express 3.0 x16 and operates at a 250 W power consumption level, using passive cooling for efficient, quiet performance. It supports FP16 and FP32 data types, making it suitable for various computational workloads.

Additionally, there is a server-specific variant of this GPU that forgoes PCI connectivity in favor of NVLink. This alternative interface allows multiple P100 GPUs to be linked directly, enhancing multi-GPU setups in high-performance environments.

Quadro P5000

This NVIDIA GPU is built on the Pascal architecture and was introduced in 2016. It uses GDDR5X memory, providing a solid memory bandwidth of 288 GB/s over a 256-bit interface. With 2,560 CUDA cores, it’s designed for powerful parallel processing, though it does not include tensor cores.

Quadro P5000 GPU connects through PCI Express 3.0 x16, drawing a manageable 180 W of power, and features active cooling to keep it running efficiently. It primarily supports FP32 data types, making it well-suited for applications needing reliable floating-point calculations.

Quadro GP100

This NVIDIA GPU, based on the Pascal architecture from 2016, is equipped with HBM2 memory, offering a high memory bandwidth of 717 GB/s over a 4096-bit interface. It has 3,584 CUDA cores but does not include tensor cores, focusing instead on versatile parallel processing.

Connecting via PCI Express 3.0 x16, it runs at a power level of 235 W and uses active cooling to maintain optimal performance. Quadro GP100 supports both FP16 and FP32 data types, making it suitable for a range of computational workloads.

For users seeking even higher performance, two Quadro GP100 GPUs can be linked through NVLink, enabling a combined 32 GB of memory and scaled-up processing power.

Tesla V100 for PCIe 16 GB

This NVIDIA GPU, built on the Volta architecture and launched in 2017, is designed for high-performance computing. It features HBM2 memory with an impressive bandwidth of 900 GB/s across a 4096-bit interface, delivering exceptional memory access speed.

With 5,120 CUDA cores and 640 tensor cores, this GPU is optimized for heavy parallel workloads and excels in tasks requiring deep learning and AI. It connects through PCI-Express 3.0 x16 and consumes 250 W of power, using passive cooling for efficient operation.

It supports multiple data types, including INT32 and FP32, and its tensor cores are optimized for FP16 precision, enabling faster processing in machine learning applications.

Quadro RTX 5000

This NVIDIA GPU is built on the Turing architecture, introduced in 2018, and features GDDR6 memory with a memory bandwidth of 448 GB/s on a 256-bit interface. With 3,072 CUDA cores and 384 second-generation tensor cores, it’s designed to handle complex computational tasks, including AI and deep learning.

Connecting via PCI Express 3.0 x16, it operates at a power consumption of 265 W and is actively cooled to maintain performance under heavy workloads. The GPU supports INT32 and FP32 data types, while its tensor cores are optimized for INT4, INT8, and FP16 precision, delivering greater speed and efficiency in specialized tasks.

For users needing even more power and memory, two Quadro RTX 5000 GPUs can be linked through NVLink, expanding combined memory to 32 GB and scaling up performance for demanding applications.

T4

This GPU, based on NVIDIA’s Turing architecture and released in 2018, is equipped with GDDR6 memory, offering a memory bandwidth of 320 GB/s across a 256-bit interface. With 2,560 CUDA cores and 320 second-generation tensor cores, it’s designed to handle both general-purpose and AI-focused computing tasks.

The GPU connects through PCI-Express 3.0 x16, operates at a power rating of 70 W, and utilizes passive cooling to maintain efficiency quietly. T4 supports INT32 and FP32 data formats, while its tensor cores are optimized for INT4, INT8, and FP16 precision, enhancing performance for machine learning and specialized calculations.

RTX A4000

Built on the advanced Ampere architecture and released in 2021, this NVIDIA GPU features GDDR6 memory with a substantial bandwidth of 448 GB/s over a 256-bit interface. With 6,144 CUDA cores and 192 third-generation tensor cores, it’s engineered to deliver high performance for intensive computational tasks, including deep learning and AI workloads.

Connecting through PCI-Express 4.0 x16, RTX A4000 GPU consumes 140 W and uses active cooling to maintain optimal operation. Its third-generation tensor cores support a range of precisions — TF32, FP16, BF16, INT8, and INT4 — providing flexibility and efficiency across diverse data-intensive applications, from scientific computing to advanced machine learning.

A2 PCIe

This NVIDIA GPU, designed with the Ampere architecture and introduced in 2021, utilizes GDDR6 memory with a memory bandwidth of 200 GB/s across a 128-bit interface. It features 1,280 CUDA cores and 40 third-generation tensor cores, delivering solid performance for tasks requiring efficient parallel processing and AI capabilities.

It connects through via PCI-Express 4.0 x8, it operates with a low power draw of just 60 W and relies on passive cooling, making A2 an energy-efficient choice for quieter, cooler systems. The tensor cores support multiple precision types — TF32, FP16, BF16, INT8, and INT4 — ensuring flexibility for diverse workloads, including machine learning and inference tasks.

GeForce RTX 4080

This NVIDIA GPU, built on the Ada Lovelace architecture and released in 2022, features high-speed GDDR6X memory with a bandwidth of 716.8 GB/s across a 256-bit interface. Equipped with 9,728 CUDA cores and 304 fourth-generation tensor cores, it is designed for demanding computational tasks, including advanced AI and deep learning applications.

With a PCI-Express 4.0 x16 connection, GeForce RTX 4080 operates at 320 W and utilizes active cooling to ensure stable performance. The GPU supports data types like INT32 and FP32, while its tensor cores provide flexible precision options, including FP8, FP16, BF16, TF32, INT8, and INT4, enabling optimized performance across a variety of workloads and scientific computing applications.

GeForce RTX 4060 Ti 16 GB

This 2023 NVIDIA GPU, powered by the Ada Lovelace architecture, comes with GDDR6 memory, delivering a memory bandwidth of 288 GB/s over a 128-bit interface. It includes 4,352 CUDA cores and 136 fourth-generation tensor cores, designed for high-efficiency computing and AI-driven tasks.

Connecting via PCI-Express 4.0 x8, GeForce RTX 4060 Ti 16 GB runs at 165 W with active cooling to maintain optimal temperatures under heavy workloads. The GPU supports INT32 and FP32 data types, and its tensor cores provide versatile precision support for FP8, FP16, BF16, TF32, INT8, and INT4, making it ideal for diverse applications, including training deep learning models and running inference.

GeForce RTX 4070 Ti SUPER

This 2024 NVIDIA GPU, utilizing the Ada Lovelace architecture, is equipped with GDDR6X memory, providing a substantial memory bandwidth of 672.3 GB/s over a 256-bit interface. It features 8,448 CUDA cores and 264 fourth-generation tensor cores, built for advanced computing tasks and AI applications among others.

It connects through PCI-Express 4.0 x16, it operates at 285 W with active cooling for sustained high performance. Supporting INT32 and FP32 data types, its tensor cores also offer flexible precision options, including FP8, FP16, BF16, TF32, INT8, and INT4, making GeForce RTX 4070 Ti SUPER ideal for a wide range of workloads, including machine learning.

GeForce RTX 4080 SUPER

This 2024 NVIDIA GPU, built on the Ada Lovelace architecture, is equipped with high-speed GDDR6X memory, achieving a bandwidth of 736.3 GB/s over a 256-bit interface. With 10,240 CUDA cores and 320 fourth-generation tensor cores, it’s crafted to tackle advanced processing demands, especially in AI and scientific computing.

It connects via PCI-Express 4.0 x16, operating at 320 W with active cooling to ensure stable performance under intense workloads. Supporting INT32 and FP32 data types, its tensor cores also allow for flexible precision across FP8, FP16, BF16, TF32, INT8, and INT4, making GeForce RTX 4080 SUPER versatile for a broad range of high-performance applications.

RTX 2000 Ada Generation

This 2024 NVIDIA GPU, leveraging the Ada Lovelace architecture, comes with GDDR6 memory, offering a bandwidth of 224 GB/s over a 128-bit interface. It includes 2,816 CUDA cores and 88 fourth-generation tensor cores, optimized for efficient computing and AI-driven tasks.

With a PCIe 4.0 x8 connection, RTX 2000 operates at a low power consumption of 70 W and utilizes active cooling for reliable performance. The GPU supports INT32 and FP32 data types, while its tensor cores provide a range of precision options — FP8, FP16, BF16, TF32, INT8, and INT4 — making it suitable for various applications, from machine learning to data-intensive workloads.