Graphics Processing Units (GPUs) have become essential for modern machine learning workloads. Their parallel processing capabilities make them ideal for heavy numerical calculations common in both traditional ML and large language models (LLMs). For a deeper understanding of GPU architecture, terminology, and utilization, check out our comprehensive GPU Glossary and GPU utilization guide.

Understanding GPU Requirements for ML

Memory (VRAM) Requirements

• Large Language Models: 40GB+ for models like Llama 70B
• Image Generation: 16GB+ for models like SDXL
• Traditional ML: Often 8-16GB is sufficient
• Data Processing: Sometimes GPU memory isn’t the bottleneck

Beyond VRAM: Other Critical GPU Characteristics

While VRAM capacity often gets the most attention, several other GPU characteristics can significantly impact machine learning performance:

Memory Bandwidth

Memory bandwidth determines how quickly data can move between VRAM and the GPU’s compute units. For example:

• H200’s HBM3e memory provides 4.8 TB/s bandwidth
• A100’s HBM2e offers 2 TB/s
• L40S’s GDDR6 delivers 864 GB/s

Memory bandwidth becomes a bottleneck in several scenarios:

1. Large model inference: When running models that barely fit in VRAM:

   • Weight loading becomes more frequent
   • Memory swapping may occur
   • Cache misses increase

2. Multi-GPU training: When scaling across multiple GPUs:

   • Weight updates require frequent memory access
   • Gradient communication needs high bandwidth
   • Data loading can become memory-bound

This can be a bottleneck for multi-GPU workloads (e.g. training large models).

GPU Interconnect

Multi-GPU workloads depend heavily on inter-GPU communication:

• NVLink provides high-bandwidth GPU-to-GPU connections
• NVSwitch enables all-to-all GPU communication
• PCIe connections offer lower bandwidth but more flexibility

For instance, when running large models like Llama 3 405B across multiple GPUs, the interconnect speed can become the primary bottleneck.

Compute Architecture

Different GPU generations offer varying features:

• Tensor Cores: Specialized for matrix multiplication
• Ray Tracing (RT) Cores: Accelerate ray tracing operations
• Clock speeds: Affect raw computing power
• Cache hierarchy: Impact data access speeds

As discussed in our GPU utilization guide, maximizing GPU performance requires understanding and optimizing for all these characteristics, not just VRAM capacity.

Matching GPUs to ML Tasks

Image and Video Processing

• Image Generation: H100 or A100 recommended
• Video Processing: L40S or H100
• OCR/Computer Vision: L40S sufficient

Traditional ML

• Training: A100 or L40S
• Inference: L40S often sufficient
• Data Processing: Consider CPU for some tasks

Language Models

• Large Models (70B+): H100 or H200
• Medium Models (7-70B): A100 80GB
• Small Models (less than 7B): A100 40GB or L40S

Top 5 GPUs for Machine Learning

1. NVIDIA L40S

Best value for many ML tasks, with excellent availability.

• VRAM: 48GB GDDR6
• Performance: Strong for traditional ML
• Best For: Computer vision, smaller LLMs
• Availability: Excellent
• Cost: Lower than A100
• ROI: Best for most common ML tasks

2. NVIDIA A100 40GB

Balanced option for medium-scale workloads.

• VRAM: 40GB HBM2e
• Performance: Similar to L40S
• Best For: Medium-sized models
• Availability: Very good
• Cost: Similar to L40S
• ROI: Good for specific workloads

3. NVIDIA H100

The current industry standard for high-end ML.

• VRAM: 80GB HBM3
• Performance: Excellent for all ML tasks
• Best For: Model training, inference for larger models
• Availability: Generally available
• Cost: High but justified for heavy usage
• ROI: Good for large-scale training, production workloads

4. NVIDIA A100 80GB

Proven workhorse for ML workloads.

• VRAM: 80GB HBM2e
• Performance: Strong for most tasks
• Best For: Large model inference
• Availability: Widely available
• Cost: Lower than H100
• ROI: Excellent for most use cases

5. NVIDIA H200

The newest and most powerful GPU, but limited availability.

• VRAM: 141GB HBM3e
• Performance: 1.9x faster than H100
• Best For: Largest models, cutting-edge research
• Availability: Limited, not widely accessible
• Cost: Premium pricing
• ROI: Best for specific high-end needs

Performance Comparison

Accessing GPU Computing

While you can purchase these GPUs directly, most organizations are better served by cloud GPU providers. Options include:

1. Major Cloud Providers:

   • AWS
   • Google Cloud

2. Specialized GPU Providers:

   • Modal
   • RunPod
   • Lambda Labs

Why Choose Modal for GPU Computing?

Modal offers several advantages for ML workloads:

1. Instant Access: No waiting for GPU availability
2. Automatic Scaling: Pay only for what you use
3. Simple Deployment: Python-native interface
4. Cost Effective: No long-term commitments

Example: Running ML on Modal

import modal

app = modal.App("ml-workload")

@app.function(gpu="A100")
def train_model():
    # Your ML code here
    pass

Ready to start running ML workloads on powerful GPUs? Try Modal free or check out our documentation for more examples.

Additional Resources

• Modal’s GPU Glossary - Comprehensive guide to GPU terminology
• GPU Utilization Guide - Deep dive into maximizing GPU performance
• Cold Start Guide - Tips for reducing model startup times
• H100 vs. A100 - Comparing H100 and A100 GPUs
• Future of AI Infrastructure - Trends in GPU computing