While building or upgrading a deep learning workstation, the question of how many GPUs you need is more crucial than whether you need.
Whether you’re training GANs, fine-tuning transformers, or deploying computer vision models, the right deep learning GPU configuration is vital. Before diving into specs and budgets, it’s important to understand how GPU count affects performance, scalability, cost, and long-term flexibility.
In this blog, we’ll go over how to choose best GPU configuration for deep learning based on your workload, budget, and project goals to avoid overkill or causing bottlenecks. Although, we can help you cut through the clutter and build wisely.
Why Do GPUs Matter for Deep Learning?
Deep learning models are computationally demanding.
GPUs are made for parallelism, as opposed to CPUs, which are built for sequential computing. Furthermore, they are perfect for matrix operations at the core of neural networks since they can do thousands of operations simultaneously.
The ability to handle complex models and large datasets is crucial in real life since it results in faster training times and iteration cycles. The power of your GPUs increases the capacity of your workstation to handle the demands of modern AI activities.
Also Read: How to Find Best GPU for Deep Learning
Is One GPU Enough for Deep Learning?
You can’t get any universal answer for how many GPUs you need. The ideal number depends on various factors:
1. Distributed Training Framework:
Frameworks for modern deep learning allow for distributed training across several GPUs, that improves scalability as well as efficiency.
2. Model Complexity:
You can choose GPU resources based on following model complexity –
Basic Models: One NVIDIA GPU for deep learning, like the RTX 4090, is sufficient for simple CNNs or basic feedforward networks.
Complex Models: Due to their high processing requirements, many GPUs are very helpful for larger models like deep convolutional networks (like ResNet, and VGG) or transformer-based models (like BERT, and GPT).
3. Dataset Volume:
Check below for GPU resources based on dataset size that ensures efficient training and balanced processing performance –
Small Datasets: A single GPU can effectively process a small dataset.
Big Datasets: A GPU cluster for deep learning can increase training speed and assist spread of the data processing burden for very large datasets.
4. Batch Sizes:
Explore the following batch sizes to pick the right GPC resources for your need –
Large Batch Size: Large batch sizes have the potential to speed up training, and also use more GPU memory. Many GPUs can assist handle higher batch sizes. This is because they divide the burden.
Small Batch Size: A single GPU with enough memory may be sufficient for smaller batch sizes.
5. Budget Constraints:
The number of GPUs comes with higher cost, So, balance the number of GPUs within your budget.
6. Resource Availability:
The availability of GPU resources will influence your decision.
7. Time Constraints:
Whether you’re racing against the clock or have flexible timelines, scale GPU usage to match your model training needs more efficiently.
Time-Sensitive Training: Using several GPUs can significantly cut down on training time if you need to train your model rapidly.
Flexible Time: Although training will take longer, fewer GPUs may be enough if you don’t have any time limits.
When is One GPU Enough?
When you’re just starting. A single GPU might be sufficient for your all needs. Here is why:
1. Proof of Concept Projects:
One strong deep learning GPU, such as the Nvidia RTX 4090 or the A100, may perform most tasks for academic research or early-stage prototyping. Furthermore, it’s easy to test deployment techniques, experiment with topologies, and train medium-sized models.
2. The Budget Factors
High-end graphics processing units are inexpensive. The price range for a workstation with a single high-end Nvidia GPU for deep learning is affordable. Meanwhile, it’s a clever method to get started small, learn, and grow as necessary.
3. Condensed and Functional
A single GPU arrangement uses less power, generates less heat, and is easier to maintain. This is more important than you may imagine if you work from a shared lab or a tiny home office.
When Do Two GPUs Make Sense?
A significant power boost can be obtained by upgrading to two GPUs, which is frequently the ideal configuration for experts who frequently train larger models.
1. More Rapid Training
Your training time can be almost halved with two GPUs-provided your code is parallelized. Multi-GPU training is supported by frameworks like PyTorch and TensorFlow, which use tools like DistributedDataParallel and DataParallel.
2. More Experiments
Using several GPUs enables you to conduct multiple experiments at once. Parallel testing of various architectures or hyperparameters speeds up research and iteration.
3. Equitable Investment
Two GPUs still reduce complexity and expenses when compared to a 4-GPU configuration. While a full-fledged server seems like overkill, it’s a sensible next step when a single GPU is holding you back.
When are four or more GPUs Required?
A multi GPU workstation deep learning setup with 4+ cards becomes essential in certain use cases.
1. Training Models on a Large Scale
Working with large transformers (such LLMs like GPT or BERT), computer vision systems with billions of parameters, or reinforcement learning in challenging contexts will require a significant amount of processing power. In order to scale beyond what a single card can manage, multi-GPU configurations enable model parallelism and pipeline parallelism, two crucial techniques.
2. High Workloads in Production
It will be advantageous to have numerous GPUs operating around-the-clock if your team is frequently training models, optimizing pretrained networks, and implementing AI at magnitude. Continuous model delivery is made possible by this configuration, which also facilitates real-time workflows.
3. AI On-Premises Systems
To avoid internet congestion, preserve data privacy, or reduce long-term cloud expenses, some businesses opt for on-premise GPU servers. A localized AI cloud is a system with four to eight GPUs but without the monthly AWS fees.
4. Preparing Future
A multi-GPU setup could be unnecessary now, but it might be necessary tomorrow. This is a wise long-term investment if you want to prevent having to rebuild your workstation every 12 to 18 months due to an increasing workload.
Which GPU is best for Deep Learning?
Well, numerous GPUs can be used for deep learning. However, the majority of the best GPUs are from NVIDIA. All of our recommendations will belong to NVIDIA. This is because NVIDIA has some of the best and highest-quality GPUs in the market right now.
Check below to get the reference for highest quality NVIDIA GPUs for deep learning. These top choices will help you whether you want to start with a consumer-grade GPU to dabble in deep learning, jump in with our recommendation for a top-tier data center GPU, or even make the move to a managed workstation server.
Although the number of GPUs you purchase for a deep learning workstation may vary, it is generally best to try to attach as many as possible to your deep learning model. Your best bet for deep learning will be to start with at least four GPUs.
- NVIDIA RTX 4090 – Top-tier consumer GPU
- NVIDIA RTX 5090 – Latest-gen performance powerhouse
- NVIDIA RTX A6000 – Reliable choice for heavy workloads
- NVIDIA RTX 6000 Ada – High-performance with enterprise reliability
- NVIDIA Tesla A100 – Data center-grade deep learning GPU
- NVIDIA L40s – Ideal for large models and generative AI workflows
Also Read: GPU vs CPU – Which One if Best for Image Processing?
Concluding Up:
Choosing the best GPU configuration for deep learning workstation is essential to optimizing performance, scalability, and cost. Additionally, a single GPU may be sufficient for basic workloads and proof-of-concept projects. Upgrading to two or multi-GPUs setup is necessary for handling larger datasets, complicated models, and faster training periods.
Furthermore, for demanding applications like large-scale model training or AI deployment, a multi-GPU configuration, which can comprise four to eight GPUs, can significantly boost efficiency.
In the end, your unique requirements, financial constraints, and project objectives will determine the best GPU arrangement. You may make a well-informed, future-proof choice for your deep learning journey by striking a balance between these variables.
