With AI tools becoming more readily available to everyone, many programmers, makers, and researchers are beginning to run machine learning models and local LLMs on their own computers instead of using the cloud solely.

No matter if you’re trying out local AI chatbots, training ML models, or making applications with AI-enabled features, the type of GPU you choose will play a critical role in how well your local LLM or machine learning function runs.

In this article, we explore some examples of the best GPUs for AI workloads today. These examples cover creating ML models and running local LLMs. 

Why GPUs are Important for AI Workloads

AI models require high-speed computations due to the size of the data sets and also the complexity of the neural networks.

GPUs are perfect for this task because they provide the following:

- Significant parallel processing power

- High memory bandwidth

- Specialized AI acceleration cores

- Higher training/inference speeds

Because of these advantages, GPUs are an essential part of any AI development project, deep learning research, or local LLM testing. 

What GPU Specs Matter Most for AI?

When choosing a GPU to perform AI tasks, some specifications are more important than others.

1. VRAM Capacity: 

Having more VRAM allows for the use of larger AI models, allowing them to run smoothly across multiple GPUs.

2. Tensor / AI Accelerators: 

Recently manufactured GPUs come equipped with specialized cores that offer accelerated matrix computations leveraged heavily during the training of deep neural networks. These accelerators significantly improve both training speeds and inference speeds.

3. Memory Bandwidth: 

When performing AI workloads, there is a need for quick access to large data sets which makes memory bandwidth an essential component of efficient processing. 

A comprehensive list of specifications can be seen here to keep you on track when researching and purchasing your next GPU

Running Local LLMs: Hardware Considerations
More and more developers are running AI models locally over the last few years.

Some examples of local LLM tasks include:

- Running AI Chatbots

- Testing Language Models

- Building AI Applications

- Experimenting with Open-Source Models

A well-functioning AI setup locally will typically have:

- A GPU with good computational power

- 12 - 16 GB VRAM

- Fast NVMe storage

- Adequate system RAM

Best GPUs for AI, Machine Learning, and Local LLMs (Ranked)

1. RTX 5080 - Best High-End GPU for AI Workstations

The NVIDIA GeForce RTX 5080 is the most powerful next-generation GPU on the market, designed specifically for the most demanding applications and workloads.

Reasons Why It Is Good For AI (Artificial Intelligence):

• New Advanced Tensor Core Architecture Allows For More Efficient AI (Artificial Intelligence) Acceleration
• Excellent Performance For Machine Learning Training and Inference
• High compute capability for complex AI models
• Ideal for Developers and/or Professional Workstations

Best Use Cases:

• Advanced AI Development
• Machine Learning Training
• Running Very Large Local AI (Artificial Intelligence) Models

https://digibuggy.com/product/Gigabyte-RTX-5080-WindForce-OC-SFF-16GB-GDDR7-Graphics-Card

2. RX 9070 XT - Powerful Alternative for AI and Compute

The AMD Radeon RX 9070 XT is a powerful graphics processing unit that provides significant compute power.

Highlights of the RX 9070 XT include the following:

• Excellent raw compute performance
• Affordable pricing vs. other high-end GPUs
• Increased support for AI workloads via the ROCm ecosystem
• Ideal for workloads requiring significant compute power

Is suited for:

• GPU compute workloads
• In developing AI applications using AMD technology
• For use in developing high-performance systems.

https://digibuggy.com/product/ASRock-RX-9070-XT-Steel-Legend-16GB-GDDR6-Graphics-Card

3. RTX 5070 Ti - Balanced GPU for AI Development

The NVIDIA GeForce RTX 5070 Ti is an excellent option when considering the price-performance ratio and overall value.

The reasons to use this graphics card for AI include:

• Tensor cores capable of great acceleration
• Able to handle medium-sized ML workload
• Efficient power usage
• Good price-performance ratio

Who should use the NVIDIA GeForce RTX 5070 Ti for AI includes:

• AI developers
• ML experimentation
• Testing local models of AI

https://digibuggy.com/product/MSI-RTX-5070-Ti-MLG-Edition-OC-16GB-GDDR7

4. RTX 5060 Ti - Entry-Level GPU for Learning AI

The NVIDIA GeForce RTX 5060 Ti is an excellent entry-level option for those beginning their AI journey.

Why it is useful for beginners:

• Supports all modern AI frameworks.
• Entry-level price point for GPU-based AI.
• Ideal for smaller ML projects.
• Great for student projects or hobbyists.

Ideal for:

• Beginners learning ML.
• Small AI projects.
• Entry-level workstations for AI.

https://digibuggy.com/product/Gigabyte-RTX-5060-Ti-Windforce-Max-OC-16GB-GDDR7-Graphics-Card

Final Verdict

Selecting the appropriate GPU for artificial intelligence (AI) depends on the type of work you will be doing and available funds.

If you plan to do high-level AI development or more advanced machine learning tasks, consider using GPUs such as the NVIDIA GeForce RTX 5080, which delivers outstanding power. Other great choices include the GeForce RTX 5070 Ti for many types of AI applications and the GeForce RTX 5060 Ti for entry-level users.

If you want another option/platform when working on AI workloads that aren’t just based on Nvidia GPUs, you can choose to employ a different ecosystem using AMD GPUs such as the RX 9070 XT. 

Frequently Asked Questions (FAQ’s)

1. Which Graphics Card is Suitable for AI Development?

High-end graphics cards such as the RTX 5080 will provide good performance for artificial intelligence workloads. These cards are designed with advanced technology and have the ability to run applications that have been designed to be AI-accelerated.

2. Are AMD Graphics Cards Capable of Doing Machine Learning Workloads?

Yes, AMD graphics cards such as the RX 9070 XT can do AI workloads through the use of the supported frameworks and the ROCm ecosystem.

3. How Much Memory (VRAM) Does AI Workloads Require?

Most artificial intelligence workloads run well with VRAM between 12 and 16 GB of VRAM for machine learning applications, but if you are using large AI models, this will work best with graphics accelerators that have a larger memory size (32 GB VRAM or more).

4. Does Machine Learning Need a Top-End Graphics Card?

Although small ML projects can run on low-powered computer hardware, a top-end graphics card will greatly reduce the amount of time it takes to train, infer, and test machine learning algorithms using supercomputers.