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Best GPU for Deep Learning – Top 9 GPUs for DL & AI

Find out the best GPUs for deep learning, AI development and machine learning.

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Choosing the right GPU can make or break your AI project’s performance, scalability, and time-to-insight. With new GPU architectures and models being released every year, finding the best option for your deep learning use case can be challenging.

So in this guide, we have selected NVIDIA’s latest GPU lineups optimized for AI acceleration, covering high-end data center solutions to consumer and professional desktop GPUs.

Furthermore, we’ll also explain key points to consider when choosing a GPU for AI and deep learning workloads.

Things to Consider When Buying a GPU for Deep Learning and AI

  • GPU architecture generation and features: Latest architectures like Hopper, Ada Lovelace, and Ampere offer better performance, efficiency, and dedicated AI acceleration hardware like Tensor Cores.
  • Number of CUDA cores and Tensor/RT cores: More cores enable higher parallelism and throughput for compute-intensive AI workloads.
  • GPU memory capacity: Larger memory capacity is needed for training bigger and more complex deep learning models on large datasets. Which means larger is better.
  • Memory bandwidth and type: Higher bandwidth with faster memory types like HBM allows more efficient data transfer between GPU and memory.
  • Power consumption, thermals, and cooling requirements: High-end GPUs can require premium power supplies and robust cooling solutions which eventually increase the project’s cost, so make sure to take note of the power consumption.
  • Software compatibility and optimization: Ensure the selected GPU has good software/driver support and integration with frameworks required by your workflow.
  • Overall performance-per-dollar and TCO: Weigh purchase price against performance to get the best long-term value for your use case.

List of the best 9 GPUs for Deep Learning

Here are the best GPUs for deep learning and AI with their brief introduction suggesting what makes them the top choice:

  1. NVIDIA H100 – The flagship GPU for the largest AI, data center, and HPC workloads, with exceptional performance from the new Hopper architecture.
  2. NVIDIA A100 – Excellent performance and scalability for demanding AI training, HPC, and data analytics in the data center.
  3. NVIDIA RTX 4090 – Fastest consumer GPU great for AI development, offering immense CUDA and Tensor Core performance with 24GB VRAM.
  4. NVIDIA RTX A6000 – Powerful pro-grade GPU combining huge 48GB memory and Tensor Cores ideal for large AI, data science, and visualization workloads.
  5. NVIDIA RTX 3090 Ti – High-end previous-gen GPU that remains very capable for GPU rendering and deep learning training with 24GB GDDR6X memory.
  6. NVIDIA RTX 4080 – Balances top performance, 16GB VRAM, and energy efficiency well for prosumers doing AI development and 4K gaming.
  7. NVIDIA RTX A5000 – Cost-effective professional GPU accelerating a wide range of AI rendering, and data science tasks.
  8. NVIDIA RTX 3080 – Excellent value GPU providing solid deep learning and GPU rendering performance at a lower price point.
  9. NVIDIA RTX A4000 – Budget professional GPU option suitable for smaller AI, rendering workloads with 16GB VRAM.

If you want to read the summary in terms of specs and use case, scroll down to the summary section, where I’ve summarized all 9 GPUs in a table for a quick read.

Now, let’s address each GPU individually to learn more about them.

1. NVIDIA H100

NVIDIA H100, GPU for Deep Learning

The H100 is NVIDIA’s cutting-edge flagship GPU, designed for powering the largest AI workloads in data centers and HPC environments.

As the world’s first data center GPU based on the transformative Hopper architecture, it delivers exceptional performance and efficiency for tasks like training large language models.

Key features:

  • 80 billion transistors, 16,896 CUDA cores, 576 Tensor Cores
  • Up to 80GB of HBM3 memory and an insane 3TB/s memory bandwidth
  • Transformer Engine ASICs for up to 3x higher AI performance
  • Support for structural sparsity and new data formats like FP8
  • Multi-Instance GPU (MIG) partitioning for optimal utilization
  • High bandwidth memory cache to reduce data movement

Providing unparalleled computational horsepower, the H100 sets a new standard for accelerating groundbreaking AI research and ultra-scale data center workloads.

2. NVIDIA A100


The A100 was NVIDIA’s flagship data center GPU built on the Ampere architecture, purpose-built for AI, data analytics, and HPC applications. It provides excellent performance and multi-instance GPU capabilities for training large deep-learning models.

Key features:

  • 6,912 CUDA cores, 432 Tensor Cores
  • Up to 80GB of HBM2e memory and 1.9TB/s bandwidth
  • Multi-Instance GPU (MIG) partitioning for optimal utilization
  • Third-gen Tensor Cores with sparsity support
  • Optimized for popular ML frameworks like PyTorch, TensorFlow
  • Supports structural sparsity for higher throughput

While superseded by Hopper, the A100 remains a great option for large-scale deep learning and high-performance computing workloads.

3. NVIDIA RTX 4090


NVIDIA’s flagship Ada Lovelace GPU, the RTX 4090, brings tremendous AI acceleration capabilities to desktops and workstations. It is a favorite choice for hardcore gamers who don’t want to compromise with specs.

It’s the fastest consumer GPU for AI development, training, and inference tasks.

Key features:

  • 16,384 CUDA cores, 512 Tensor Cores
  • 24GB of ultrafast GDDR6X memory and 1TB/s bandwidth
  • 3rd gen RT Cores and 4th gen Tensor Cores
  • AI-assisted DLSS 3, performance-boosting technologies
  • AV1 encoder for enhanced video capabilities

The RTX 4090 provides unrivaled performance in a consumer form factor, making it ideal for GPU rendering, AI dev work, and gaming.



A professional-grade powerhouse, the RTX A6000 combines huge memory capacity with Tensor Core acceleration ideal for large-scale AI, data science, and visualization workloads.

Key features:

  • 10,752 CUDA cores, 336 Tensor Cores
  • Massive 48GB GDDR6 memory capacity
  • 768 GB/s memory bandwidth in 300W TDP
  • Built on optimized Ampere architecture
  • Ray Tracing and AI acceleration for pro apps
  • ECC memory support for higher data integrity

With immense compute muscle and memory, the A6000 excels at professional GPU rendering, AI training/inference, and data visualization.

5. NVIDIA RTX 3090 Ti


While a previous-generation GPU, the flagship RTX 3090 Ti, based on Ampere architecture, still packs a serious punch for GPU rendering and deep learning training.

Key features:

  • 10,496 CUDA cores, 328 Tensor Cores
  • 24GB of ultrafast GDDR6X VRAM
  • 1,008 GB/s of memory bandwidth
  • Great blend of VRAM and Tensor Core performance

The RTX 3090 Ti remains an extremely capable AI training accelerator and GPU rendering solution at a lower cost than the latest gen.

6. NVIDIA RTX 4080


A high-end GPU in the Ada Lovelace family, the RTX 4080 strikes an excellent balance of performance, VRAM capacity, and energy efficiency for prosumers.

Key features:

  • 9,728 CUDA cores, 304 Tensor Cores
  • 16GB of GDDR6X VRAM and 717 GB/s bandwidth
  • Advanced AI acceleration and rendering capabilities
  • Support for DLSS 3, DLDSR, AV1 encoding
  • More affordable than the flagship RTX 4090
  • Ada Lovelace streaming multiprocessors

The RTX 4080 provides ample horsepower for AI development, deep learning training, and gaming in a relatively power-efficient package.


The RTX A5000 is a cost-effective professional GPU designed to accelerate a wide variety of AI, rendering, and data science workloads.

Key features:

  • 8,192 CUDA cores, 256 Tensor cores
  • 24GB GDDR6 VRAM and 768 GB/s bandwidth
  • Built on Ampere architecture and optimized for pro apps
  • Provides great performance at lower power/cost

For professional users looking for a balanced blend of AI acceleration, rendering prowess, and energy efficiency, the A5000 delivers.

8. NVIDIA RTX 3080


While from a previous generation, the RTX 3080 still provides excellent value as a lower-cost option for deep learning, GPU rendering, and gaming.

Key features:

  • 8,704 CUDA cores, 272 Tensor Cores
  • 10GB GDDR6X VRAM and 760 GB/s bandwidth
  • Tensor Core acceleration plus ray tracing
  • Great performance for AI training and GPU tasks
  • Affordable for prosumers and enthusiasts

The RTX 3080 packs solid acceleration horsepower into a relatively affordable GPU well-suited for AI development and other demanding workloads.


An affordable professional GPU, the RTX A4000 packs enough performance and memory capacity to accelerate smaller AI, rendering, and data science workloads.

  • 6,144 CUDA cores, 192 Tensor cores
  • 16GB GDDR6 VRAM and 448 GB/s bandwidth
  • Lower 140W TDP optimized for efficiency
  • Supports GPU virtualization and app streaming
  • Suitable for entry-level AI acceleration

The RTX A4000 provides a budget-friendly way for professionals to tap into GPU acceleration for compatible workloads.


Here’s a quick summary of all the 9 GPUs which we discussed earlier:

GPUArchitectureCUDA CoresTensor CoresMemoryMemory BandwidthTDPBest Use Case
NVIDIA H100Hopper16,896576Up to 80GB HBM33 TB/s700WLargest AI/data center/HPC workloads
NVIDIA A100Ampere6,912432Up to 80GB HBM2e1.9-2.0 TB/s400WLarge-scale DL, HPC, scientific computing
NVIDIA RTX 4090Ada Lovelace16,38451224GB GDDR6X1.0 TB/s450WHigh-end AI dev/training, GPU rendering
NVIDIA RTX A6000Ampere10,75233648GB GDDR6768 GB/s300WAI, data science, visualization workloads
NVIDIA RTX 3090 TiAmpere10,49632824GB GDDR6X1.0 TB/s450WAI training, GPU rendering
NVIDIA RTX 4080Ada Lovelace9,72830416GB GDDR6X716.8 GB/s320WHigh perf AI dev/training, gaming
NVIDIA RTX A5000Ampere8,19225624GB GDDR6768 GB/s230WAI, rendering, data science workloads
NVIDIA RTX 3080Ampere8,70427210GB GDDR6X760 GB/s320WAI training, GPU rendering, gaming
NVIDIA RTX A4000Ampere6,14419216GB GDDR6448 GB/s140WEntry AI, rendering, smaller datasets

Every GPU has its own use case. If you go with the highest speced one, then you will need a lot of energy and enough cooling to cool down the beast.

So make sure to identify your needs first, such as your workload demands. It won’t make any sense to pay more and not get the most out of it.

Let us know your thoughts on what GPU you have or planning to buy.

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A tech journalist whose life revolves around networks.

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