Guide to Hardware Requirements for Training and Fine-Tuning Large Language Models

Author(s): Sanket Rajaram

Originally published on Towards AI.

The Ultimate Guide to Hardware Requirements for Training and Fine-Tuning Large Language Models (LLMs)

The rapid evolution of Artificial Intelligence has led to the emergence of Large Language Models (LLMs) capable of solving complex tasks and driving innovations across industries.

However, training and fine-tuning these models demand substantial computational power. Whether you’re an AI enthusiast, a researcher, or a data scientist, understanding the hardware requirements for LLMs is crucial for optimizing performance and cost-effectiveness.

In this comprehensive guide, we delve into the essential hardware setups needed for training and fine-tuning LLMs, from modest 7B/8B models to cutting-edge 70B models, to help you achieve your AI ambitions.

Training Large Language Models

1. Training Resource Estimates for a 7B/8B Model

Model Size:
Parameter Count: ~7 billion.
Memory Usage:
— Full Precision (FP32): ~28GB.
— Mixed Precision (FP16): ~14GB.

Hardware Requirements:
1. GPU Memory:

• Minimum Setup:
  4 GPUs with 16GB VRAM each (e.g., NVIDIA RTX 3090, 4090, or A100 40GB).
• Ideal Setup:
  2–4 A100 GPUs (40GB each) for faster training and larger batch sizes.

2. Compute Time:

• Example:
  Training on 1 trillion tokens: ~1 month on 8 A100 GPUs (40GB each).

3. Storage:

• Datasets: ~1–5TB for text data.
• Checkpoints: ~500GB for saving intermediate states.
• RAM: At least 128GB for preprocessing and training support.
• Networking: High-speed connections (10Gbps or higher) for distributed setups.

4. Cost Estimate:

• Cloud Setup:
  — Instance: 4x A100 GPUs.
  — Cost: ~$5–$8/hour.
  —Total: ~$15,000–$30,000 for 1 trillion tokens.

2. Training Resource Estimates for a 70B Model

Model Size:
Parameter Count: ~70 billion.
Memory Usage:
— Full Precision (FP32): ~280GB.
— Mixed Precision (FP16): ~140GB.

Hardware Requirements:
1. GPU Memory:

• Minimum Setup: 16 GPUs with 40GB VRAM each (e.g., NVIDIA A100 40GB).
• Ideal Setup: 32 A100 GPUs (40GB each) for efficient training.

2. Compute Time:

• Example: Training on 1 trillion tokens:
  — ~2–3 months on 16 A100 GPUs (40GB each).
  — ~1 month on 32 A100 GPUs.

3. Storage:

• Datasets: ~10–20TB for large-scale text data.
• Checkpoints: ~2TB or more for intermediate states.
• RAM: At least 256GB; 512GB is ideal.
• Networking: High-speed interconnects like NVIDIA NVLink or Infiniband.

4. Cost Estimate:

• Cloud Setup:
• Instance: 16x A100 GPUs.
• Cost: ~$35–$50/hour.
• Total: ~$500,000–$1,000,000 for 1 trillion tokens.

Fine-Tuning Large Language Models

1. Hardware Setup for a 70B Model

Model Memory Usage:

—FP32 Precision: 280GB.

—FP16 Precision: 140GB.

— 8-bit Quantization: 70GB.

Hardware Requirements:

• GPUs: NVIDIA A100 (40GB/80GB), H100, or multiple RTX 3090/4090 GPUs with NVLink. At least 8 GPUs with 40GB VRAM or 4 GPUs with 80GB VRAM.
• CPU: High-core count CPU (e.g., AMD Threadripper or Intel Xeon) for data preprocessing.
• RAM: Minimum 256GB for handling large datasets and model offloading.
• Storage: At least 8TB NVMe SSD for dataset storage and model checkpoints.
• Networking: High-speed networking (10Gbps+) for multi-node setups.

Recommended Cloud Setup:

• Use cloud providers like AWS, Azure, or Google Cloud for access to A100/H100 GPUs.
• Examples:
  — AWS EC2: P4d or P5 instances with 8x A100 GPUs.
  — Google Cloud: A2 Mega GPU instances.

2. Hardware Requirements for 7B/8B Models

Memory Usage:
— 16-bit Precision (FP16): ~16GB VRAM.
— 8-bit or 4-bit Quantization: ~8GB VRAM.

Hardware Requirements:

• GPU:
  — Single GPU Setup:
  NVIDIA RTX 3090/4090 (24GB VRAM). OR
  NVIDIA A5000/A6000 (24GB–48GB VRAM). OR
  Dual GPU Setup (for larger batch sizes or faster training):
  — NVIDIA RTX 3080 Ti, 3090, or 4090 with NVLink or multi-GPU.
• Budget GPUs (with quantization or offloading):
  RTX 3060 (12GB VRAM), RTX 3070 Ti (8GB VRAM).
• CPU: Multi-core CPU for data preprocessing and background tasks.
  —Recommended: AMD Ryzen 7/9, Intel Core i7/i9.
• RAM:
  — Minimum: 32GB (for light workloads with quantization).
  — Recommended: 64GB or more for larger datasets or CPU offloading.
• Storage:
  — Use NVMe SSDs for fast read/write operations.
  — At least 1TB for datasets, model checkpoints, and logs.
  — For larger datasets: 2TB or more.
• Power Supply:
  Ensure sufficient wattage for GPU(s):
  — Single GPU: 750W PSU.
  — Dual GPUs: 1000W PSU.
• Networking (if Distributed):
• For multi-node training: 10Gbps or higher Ethernet connections.

Key Insights and Industry Practices

• Data Scale: According to Common Crawl, in June 2023, the web crawl contained ~3 billion web pages and ~400TB of uncompressed data, highlighting the vast datasets needed for high-quality LLM training.
• Cloud vs. On-Premises: Cloud solutions offer flexibility and scalability, but on-premises setups may be cost-effective for organizations with frequent LLM training and fine-tuning needs.
• Precision Trade-offs: Quantization techniques (8-bit or 4-bit) significantly reduce memory requirements, making fine-tuning accessible to smaller setups.

Conclusion

Training and fine-tuning LLMs require substantial computational resources, but advancements in GPU technology, cloud services, and precision optimization have made these tasks more feasible.

Whether you’re building a model from scratch or tailoring a pre-trained one, understanding the hardware requirements is crucial for successful deployment. Balancing cost, efficiency, and scalability will ensure that your LLM workflows are both practical and effective.

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Published via Towards AI