How to Size Compute, GPU, Storage, Network for Generative AI

When sizing compute, GPU, storage, and network resources for generative AI (GenAI) models or large language models (LLMs), it’s crucial to account for various factors, including the model’s scale, complexity, and intended application. Below is a detailed guide on how to approach each aspect:

1. Compute Resources

CPU Considerations

• Training Phase:
• Data Preprocessing: High core counts can accelerate data preparation steps like loading, transforming, and augmenting data.
• Model Training: Though GPUs handle most of the training, CPUs coordinate tasks, handle I/O, and manage data pipelines.
• Recommendation: Multi-core processors with high clock speeds. Server-grade CPUs like AMD EPYC or Intel Xeon are preferred.
• Inference Phase:
• For inference, especially in real-time applications, CPU power is vital for handling concurrent requests.
• Recommendation: High core counts with fast single-thread performance.

Memory (RAM)

• Training Phase:
• Requires significant memory for handling large datasets, model parameters, and batch processing.
• Recommendation: At least 128GB of RAM for small models, scaling to several terabytes for large models.
• Inference Phase:
• Sufficient RAM is needed to load the model and handle multiple concurrent inferences.
• Recommendation: Memory requirements depend on model size but generally range from 16GB to 64GB for small to moderate deployments.

2. GPU Resources

Training GPUs

• Type:
• Training large models requires GPUs with substantial memory and compute capabilities.
• Recommendation: High-end GPUs like NVIDIA A100, V100, or H100 for heavy workloads. GPUs with more memory (e.g., 40GB or 80GB) allow training larger models or larger batch sizes.
• Number:
• Distributed training often utilizes multiple GPUs to reduce training time.
• Recommendation: For large models, configurations often start with 4-8 GPUs and scale up to hundreds in parallelized systems.

Inference GPUs

• Type:
• Inference can be performed on less powerful GPUs than training.
• Recommendation: GPUs like NVIDIA T4 or A10, which offer good performance-per-watt for inference tasks.
• Number:
• Depending on the load, you may need multiple GPUs to handle high-throughput or low-latency requirements.
• Recommendation: Start with a single GPU and scale based on workload requirements.

3. Storage Resources

Type

• Training Phase:
• Fast storage is critical for quickly accessing large datasets during training.
• Recommendation: NVMe SSDs are preferred for their high throughput and low latency.
• Inference Phase:
• Less demanding than training but still benefits from fast access, especially for loading models.
• Recommendation: SSDs are typically sufficient; NVMe preferred if real-time loading is required.

Capacity

• Training Phase:
• The capacity must accommodate large datasets and model checkpoints.
• Recommendation: Start with at least 1TB of storage and scale up based on dataset size.
• Inference Phase:
• Needs to store the deployed models and related data.
• Recommendation: 100GB to 500GB, depending on model size and deployment scale.

4. Network Resources

Bandwidth

• Training Phase:
• High bandwidth is essential for distributed training and efficient data transfer to/from storage.
• Recommendation: At least 10 Gbps network connections. In high-performance environments, 25 Gbps or higher might be required.
• Inference Phase:
• Needs sufficient bandwidth to handle user requests and deliver responses quickly.
• Recommendation: 1 Gbps is typically adequate for moderate workloads, higher for large-scale or real-time applications.

Latency

• Training Phase:
• Lower latency can improve the efficiency of distributed training.
• Recommendation: Aim for minimal latency, under 1ms if possible, especially for synchronous training operations.
• Inference Phase:
• Critical for real-time applications to provide quick responses.
• Recommendation: Low latency, ideally under 10ms for real-time services.

Practical Steps for Sizing:

1. Start Small and Scale: Begin with smaller, manageable configurations and scale based on monitoring and performance metrics.
2. Consider Cloud Options: Utilize cloud services (like AWS, Google Cloud, or Azure) for flexible and scalable resources without upfront capital investment.
3. Hybrid Architectures: Combine on-premises and cloud resources to balance cost, performance, and scalability needs.
4. Evaluate Load and Usage Patterns: Regularly monitor system usage to adjust resources dynamically.
5. Cost-Effectiveness: Balance performance needs with budget constraints, optimizing for both resource utilization and cost.

Example Configuration for a Mid-Sized Project:

• Compute: 2x AMD EPYC or Intel Xeon CPUs with 32 cores each.
• Memory: 256GB RAM.
• GPUs: 4x NVIDIA A100 GPUs.
• Storage: 2TB NVMe SSD for high-speed access, 10TB SATA for larger data storage.
• Network: 10 Gbps Ethernet.

This setup provides a solid foundation for training and inference of mid-sized generative models, with room for scaling based on specific needs.

Conclusion

Properly sizing compute, GPU, storage, and network resources is critical to efficiently deploying generative AI and LLMs. Carefully consider the specific requirements of your project, the scale at which you will operate, and balance these against cost considerations to achieve optimal performance.