Best GPUs for Deep Learning: Considerations for Large AI Projects

Scale your AI projects with the right GPUs – discover the best options for your needs.

Best GPUs for Deep Learning: Considerations for Large AI Projects
Written by
Youssef El Manssouri
Published on
May 7, 2024
Read time
12 min

In the ever-evolving landscape of artificial intelligence (AI), GPUs (Graphics Processing Units) have emerged as the unsung heroes. These silicon workhorses, initially designed for rendering graphics in video games, now play a pivotal role in accelerating deep learning and scientific computations.

Whether you’re a seasoned data scientist, a startup founder, or an enterprise CTO, understanding the right GPU for your AI endeavors is crucial. Let’s explore why GPUs matter and how they can supercharge your large-scale AI projects.

The GPU Revolution

  1. From Pixels to Pixels Plus Brains
    • GPUs have come a long way since their pixel-pushing days. Today, they’re not just about rendering lifelike game environments; they’re about crunching complex mathematical operations at lightning speed.
    • Deep learning, with its neural networks and massive data sets, demands immense computational power. Enter GPUs—the accelerators that turn weeks of training into hours.
  2. Why GPUs?
    • GPUs excel at parallel processing. Unlike CPUs (Central Processing Units), which handle tasks sequentially, GPUs tackle multiple tasks simultaneously.
    • Deep learning models involve millions (sometimes billions) of parameters. GPUs can efficiently perform matrix multiplications, convolutions, and activations, making them ideal for training and inference.

The Quest for the Perfect GPU

As the demand for AI solutions grows, so does the need for robust GPU infrastructure. Here’s why businesses and individuals are on the hunt for the best GPUs:

  1. Scale Matters
    • Large AI projects require scalability, whether in natural language processing, computer vision, or drug discovery. You need GPUs that can handle not just one model but an entire fleet of them.
    • Imagine training a language model on a massive corpus of text or analyzing medical images across thousands of patients. That’s where scalability becomes critical.
  2. Speed Is Everything
    • Time is money, especially in AI research and development. Faster training means quicker insights and shorter time-to-market.
    • The right GPU can shave days off your training cycles. It’s like having a high-speed express lane for your neural networks.
  3. Accuracy and Precision
    • Deep learning models thrive on precision. GPUs that support mixed-precision training strike a balance between accuracy and speed.
    • Tensor Cores, found in NVIDIA GPUs, accelerate matrix operations while maintaining numerical stability.

Understanding Deep Learning Workloads: Unleashing the AI Beast

What Is Deep Learning?

Deep learning is the heavyweight champion of machine learning. It’s the realm where neural networks—inspired by the human brain—learn from data to make predictions, recognize patterns, and solve complex tasks. Here’s the lowdown:

  1. Neural Networks at Play
    • Imagine a vast interconnected web of artificial neurons. Each neuron processes information, passes it to others, and collectively learns from examples.
    • Deep learning models stack layers of these neurons, creating deep neural networks (hence the name).
  2. Computational Hunger
    • Deep learning models are voracious eaters of computation. They crave massive amounts of matrix multiplications, convolutions, and activations.
    • Training a deep neural network involves adjusting millions of parameters to minimize prediction errors. This process requires intense numerical computations.

The Computational Appetite of Deep Learning

  1. Model Complexity Matters
    • Shallow vs. Deep Models
      • Shallow models have few layers, making them computationally lighter. They’re like sprinters—fast but limited in depth.
      • Deep models resemble marathon runners. They explore intricate features but demand more computational stamina.
    • Architectures
      • Convolutional Neural Networks (CNNs) for image tasks, Recurrent Neural Networks (RNNs) for sequences, and Transformers for natural language processing have varying complexities.
      • The more layers and parameters, the hungrier the model.
  2. Data Size and Training Iterations
    • Big Data, Big Appetite
      • Deep learning thrives on large datasets. More data means better generalization.
      • However, feeding this data to the model during training requires substantial computational resources.
    • Epochs and Backpropagation
      • Training occurs in epochs (iterations). Each epoch fine-tunes the model’s weights.
      • Backpropagation computes gradients and adjusts parameters. The more epochs, the more computations.
      • Imagine training a language model on gigabytes of text or an autonomous vehicle system on terabytes of driving data.

The GPU Selection Conundrum

  1. Choose Wisely
    • Raw Power vs. Efficiency
      • Powerful GPUs (like the NVIDIA A100) excel in raw performance. They’re the muscle cars of deep learning.
      • Efficient GPUs (like the H100) balance performance with energy consumption. They’re the hybrids.
    • Memory Matters
      • Large models demand ample memory (VRAM). If your model doesn’t fit, training stalls.
      • High memory bandwidth ensures data flows smoothly between GPU and memory.
  2. Budget and Scale
    • Cost-Performance Trade-offs
      • Consider the total cost of ownership (TCO). Initial purchase cost, power bills, and maintenance add up.
      • Scalability matters too. Can you easily add more GPUs as your AI projects grow?

Key Factors to Consider When Choosing GPUs for Deep Learning

1. Performance Metrics: The Horsepower Under the Hood

FLOPS (Floating-Point Operations per Second):

  • FLOPS measures the raw computational power of a GPU. Think of it as the engine’s horsepower.
  • High FLOPS means faster matrix multiplications, convolutions, and activations during training and inference.
  • For large-scale AI tasks, prioritize GPUs with impressive FLOPS.

Memory Bandwidth:

  • Imagine data flowing through a pipeline. Memory bandwidth determines how much data can pass through in a given time.
  • Large models demand high memory bandwidth. It’s like widening the pipeline to accommodate more traffic.
  • GPUs with ample memory bandwidth ensure smooth data movement between VRAM and processing units.

CUDA Cores:

  • CUDA cores are the worker bees within a GPU. They execute parallel tasks.
  • More CUDA cores mean better parallelism, which speeds up computations.
  • When dealing with massive neural networks, having a swarm of CUDA cores is essential.

Throughput and Latency:

  • Throughput: How much work a GPU can handle per unit time.
  • Latency: The time it takes for a task to complete.
  • Aim for high throughput (more work done) and low latency (quick response) for seamless AI workflows.

2. Memory Capacity and Bandwidth: Fueling the AI Engine

VRAM (GPU Memory):

  • VRAM stores model parameters, gradients, and intermediate results during training.
  • Large models (BERT, GPT-3, etc.) need spacious VRAM. If your model doesn’t fit, training stalls.
  • Consider GPUs with 16GB, 32GB, or even 80GB VRAM for heavyweight tasks.

Bandwidth Bottlenecks:

  • High memory bandwidth ensures data flows smoothly between VRAM and processing units.
  • Bottlenecks slow down training. Opt for GPUs with balanced memory bandwidth to avoid choke points.

3. Mixed-Precision Computing: The Goldilocks Zone

Single Precision (FP32) vs. Half Precision (FP16):

  • Single precision: Standard floating-point format (32 bits). Accurate but computationally expensive.
  • Half precision: Compressed format (16 bits). Sacrifices some accuracy for speed.
  • Mixed-precision training combines both for faster convergence.
  • GPUs supporting mixed precision (like NVIDIA A100) strike the right balance.

4. Tensor Cores and AI-Specific Features: Turbocharging AI Workloads

NVIDIA Tensor Cores:

  • These specialized cores accelerate matrix operations (like matrix multiplication).
  • Tensor Cores boost throughput without compromising accuracy.
  • Ideal for large-scale AI tasks, especially deep learning.

Sparsity Support:

  • Sparse models (with many zero-valued weights) are common in NLP and recommendation systems.
  • Some GPUs handle sparse computations efficiently.
  • If your AI workload involves sparse matrices, look for this feature.

5. Compatibility and Software Support: The Ecosystem Matters

Framework Compatibility:

  • Ensure your chosen GPU plays well with popular deep learning frameworks (TensorFlow, PyTorch, etc.).
  • Framework-specific optimizations can significantly impact performance.

Libraries and Tools:

  • NVIDIA provides optimized libraries (cuDNN, cuBLAS) for deep learning.
  • Explore GPU-accelerated libraries for faster training and inference.

GPU Models for Large AI Projects

1. NVIDIA A100: The Titan of Tensor Cores

Architecture Overview:

  • The NVIDIA A100 is a beastly GPU based on the Ampere architecture.
  • It boasts a whopping 54 billion transistors—more than a small country’s population!
  • Tensor Cores: These specialized units accelerate matrix operations (like matrix multiplication) by combining FP16 and FP32 precision. Imagine them as turbochargers for your neural networks.

Memory Capacity:

  • The A100 flexes its muscles with up to 40GB or 80GB of high-bandwidth HBM2 memory (VRAM).
  • Large-scale AI models can comfortably stretch their legs in this memory playground.

Suitability for Large-Scale AI Training and Inference:

  • Training
    • The A100 is the go-to choice for training massive deep learning models.
    • Its Tensor Cores accelerate training, making it ideal for tasks like training language models (BERT, GPT-3) and image classifiers.
    • Researchers and data scientists drool over its performance gains.
  • Inference
    • Inference is where real-world predictions happen.
    • The A100’s low latency and high throughput ensure lightning-fast predictions.
    • Deploy it in data centers, edge devices, or even self-driving cars.

2. NVIDIA H100: The Speed Demon


  • The NVIDIA H100 is like a race car—built for speed.
  • FLOPS: It flaunts an impressive 10.6 teraflops of single-precision performance.
  • Memory Bandwidth: With 900 GB/s memory bandwidth, it guzzles data like a thirsty marathon runner.

Performance for AI Workloads:

  • AI Acceleration
    • The H100 accelerates AI workloads, including deep learning training and inference.
    • It’s a workhorse for tasks like image recognition, speech synthesis, and recommendation systems.
  • Energy Efficiency
    • The H100 balances performance and power consumption.
    • If you’re conscious of your data center’s electricity bill, the H100 is your eco-friendly companion.

3. Other Options: A Quick Glance

  • NVIDIA A6000
    • A sibling of the A100, the A6000 offers excellent performance for AI and visualization workloads.
    • It’s like a reliable sedan—it gets the job done without breaking speed records.
  • AMD Instinct MI100
    • AMD’s contender in the AI arena.
    • While not as popular as NVIDIA’s offerings, it’s worth exploring for specific use cases.

Cost Considerations and Scalability in GPU Deployment

1. Total Cost of Ownership (TCO): Navigating the Financial Cosmos

Initial Purchase Cost:

  • When acquiring GPUs, the upfront cost is like buying a spaceship. It’s a significant investment.
  • The A100, H100, or any other GPU model comes with a price tag. Consider your budget and mission requirements.

Power Consumption:

  • GPUs are power-hungry beasts. They devour electricity like starships warp through space.
  • Calculate the energy consumption based on the GPU’s wattage and usage hours.
  • Efficient GPUs (like the H100) strike a balance between performance and energy efficiency.


  • Just like spaceships need regular check-ups, GPUs require maintenance.
  • Dust them off, update drivers, and ensure proper cooling.
  • Factor in maintenance costs over time.

Long-Term Cost Savings:

  • Performance per Watt
    • Compare GPUs based on their performance-to-power ratio.
    • Efficient GPUs save you credits on your intergalactic energy bill.
  • Software Licenses and Support
    • Some GPUs come bundled with software licenses (like NVIDIA’s CUDA Toolkit).
    • Consider the value of these tools when assessing TCO.
  • Resale Value
    • GPUs depreciate over time, but some hold their value better.
    • Plan for future upgrades and consider resale options.

2. Scalability: Expanding Your Galactic Fleet

Adding More GPUs:

  • Scalability is crucial for large AI projects. Imagine your fleet of starships growing.
  • Modularity: Choose GPUs that allow easy expansion. Can you add more GPUs without disrupting the entire system?
  • Cluster Deployment: Consider GPU clusters—interconnected GPUs working together. They scale horizontally.

Cloud vs. On-Premises:

  • Cloud Services
    • Cloud providers like us here at Sesterce offer GPU instances on demand.
    • Pay-as-you-go flexibility.
  • On-Premises Infrastructure
    • Build your own GPU data center.
    • Upfront investment, but long-term control and potentially lower costs.

Real-World Use Cases

Let’s explore some real-world use cases where organizations harnessed the power of GPUs for large-scale AI initiatives. These success stories demonstrate the tangible impact GPUs can have on businesses and research:

  1. OpenAI’s GPT-3: Revolutionizing Natural Language Understandingsome text
    • Organization: OpenAI
    • GPU Model: NVIDIA A100
    • Impact
      • OpenAI’s GPT-3, powered by NVIDIA A100 GPUs, shook the AI landscape. It’s a language model with 175 billion parameters—more than any other publicly known model.
      • GPT-3’s applications range from chatbots and content generation to code completion and medical diagnosis.
      • The business impact? Improved customer interactions, personalized recommendations, and streamlined workflows.
  2. DeepMind’s AlphaFold: Decoding Protein Structuressome text
    • Organization: DeepMind (a subsidiary of Alphabet Inc.)
    • GPU Model: Custom GPUs (Tensor Processing Units)
    • Impact
      • AlphaFold, using custom TPUs (similar to GPUs), cracked the protein folding problem.
      • Accurate protein structure prediction has implications for drug discovery, disease understanding, and personalized medicine.
      • The impact? Faster drug development, reduced trial-and-error, and potential cures.
  3. NVIDIA’s Omniverse: Collaborative Virtual Worldssome text
    • Organization: NVIDIA
    • GPU Model: NVIDIA A100
    • Impact
      • Omniverse, a virtual collaboration platform, leverages A100 GPUs for real-time rendering and physics simulations.
      • Architects, designers, and engineers collaborate in shared virtual spaces.
      • The business impact? Faster design iterations, reduced physical prototyping costs, and global collaboration.
  4. Netflix’s Content Recommendation Enginesome text
    • Organization: Netflix
    • GPU Model: Custom GPUs (Netflix VMAF)
    • Impact
      • Netflix’s recommendation engine uses custom GPUs to analyze user behavior and suggest personalized content.
      • Viewers stay engaged, leading to longer subscriptions and increased revenue.
      • The impact? Improved user satisfaction and retention.
  5. Tesla’s Autopilot: Navigating the Roadssome text
    • Organization: Tesla
    • GPU Model: Custom GPUs (Tesla FSD Chip)
    • Impact
      • Tesla’s Autopilot system relies on custom GPUs for real-time image processing and decision-making.
      • Enhanced safety, reduced accidents, and the dream of fully autonomous driving.
      • The impact? Safer roads and a revolution in transportation.

Choosing the right GPU for your AI endeavors involves more than comparing specs—it’s about aligning computational power with your mission objectives. Here’s a quick recap:

  1. Performance Metrics: FLOPS, memory bandwidth, and CUDA cores matter. Seek GPUs that accelerate your neural voyages.
  2. Memory Capacity and Bandwidth: VRAM is your spaceship’s cargo hold. Ensure it’s spacious enough for your models.
  3. Mixed-Precision Computing: Balance accuracy and speed. Tensor Cores are your warp drives.
  4. AI-Specific Features: Explore Tensor Cores and sparsity support. They’re like secret stardust for your algorithms.
  5. Total Cost of Ownership (TCO): Calculate the credits—initial cost, power consumption, and maintenance. Choose wisely.
  6. Scalability: Can your fleet expand seamlessly? Think clusters and cloud vs. on-premises.

Your AI Odyssey AwaitsEvaluate your specific requirements, consider your mission parameters, and make informed decisions. Whether you’re charting the cosmos of natural language processing or decoding protein structures, GPUs are your interstellar companions.Are you ready to launch your AI project? Click here to book a call with us at Sesterce Sales—let’s discuss our GPU cloud solutions and propel your vision toward the stars!May your algorithms converge, your gradients ascend, and your neural networks illuminate the universe!