We’ve been doing a lot of hands-on experimentation with generative AI over the past year and the learning curve has been steep. GenAI doesn’t look much like other AI disciplines, from a hardware or software perspective, so there’s been an interesting assortment of topics we’ve had to learn along the way.

One of the things I had to learn was about the various datatypes, which I’ve found isn’t just a technical detail—it’s a critical factor that affects performance, accuracy, power efficiency, and even the feasibility of deploying AI models.

Understanding the datatypes used in AI isn’t just for hands-on practitioners, you often see published benchmarks and other performance numbers broken out by datatype (just look at an NVIDIA GPU data sheet). What’s it all mean?

This blog is an attempt to share what we’ve learned as we continue on this journey.

Why Data Types Matter in AI

AI operations rely on processing vast amounts of data. The format of this data directly impacts computation, memory usage, energy consumption, and model accuracy. Choosing the right data type is particularly crucial for:

• Training: A one-time process in resource-rich environments like data centers, prioritizing accuracy.
• Inference: A repetitive operation where power efficiency, memory bandwidth, and performance are paramount, especially for edge devices.

Common Data Types in AI

Floating-Point Numbers

• FP32 (32-bit floating point): The gold standard for accuracy in training. It offers high precision but at the cost of significant power and hardware requirements.
• FP16 (16-bit floating point): Reduces storage and computational demands but sacrifices some precision. Variants like bfloat16 (BF16) retain FP32’s dynamic range while reducing precision.

Integer Formats

• INT8 (8-bit integer): Popular for convolutional neural networks (CNNs) due to its efficiency and acceptable accuracy loss.
• Lower-Bit Integers (INT4, INT2, Binary Neural Networks): Emerging for specialized use cases but face challenges in achieving widespread adoption due to accuracy trade-offs.

Challenges in Selecting the Right Data Type

Accuracy vs. Efficiency

• Training uses FP32 for its precision, but inference benefits from smaller formats like INT8 to reduce memory and power usage.
• Advanced models, such as transformers, often require floating-point formats because error accumulation across layers diminishes the accuracy of integer-based computations.

Storage vs. Computation

• Data types affect both computation and storage differently. Compressed formats can reduce storage costs but require decompression for computation. For example, INT8 computations might use compressed INT4 data.

Edge vs. Data Center Environments

• Edge devices prioritize power and bandwidth efficiency, making lower-bit formats more attractive.
• Data centers can afford higher precision formats due to their superior computational resources.

Emerging Data Formats

Custom Floating-Point Variants

• NVIDIA’s FP8: Offers two versions—E5M2 (higher dynamic range) and E4M3 (higher precision). It balances accuracy and efficiency for inference but is not yet widely adopted.
• TensorFloat-32 (TF32): Used in NVIDIA GPUs for high-efficiency matrix operations.

Logarithmic Formats

• Pareto Format (Recogni): Exploits fractional exponents to eliminate multipliers, achieving significant power savings. It is proprietary and tailored for high-performance inference.

Block Floating Point

• Proposed by Microsoft and Meta, these formats share exponents across values to save storage and power. Variants like MX4 and MX6 optimize memory bandwidth while maintaining reasonable accuracy.

Mixed Data Formats: The Next Frontier

One size doesn’t fit all. Advanced AI models increasingly employ mixed data types, where different layers or operations use distinct formats. For example:

• INT8 might handle convolution layers, while FP16 processes attention mechanisms in transformers.

This mixed approach maximizes efficiency without compromising accuracy, but it requires sophisticated tools to automate data type selection.

What’s Coming?

As AI evolves, so will the quest for optimal data types. Key trends include:

1. Automation: Tools must abstract data type complexity, allowing developers to focus on AI applications rather than implementation details.
2. Specialization: Expect tailored formats for specific models, such as LLMs or edge AI.
3. Standardization: Just as INT8 became the standard for CNNs, the industry may converge on a dominant format for LLMs and transformers.

Conclusion

AI data types are more than just technical jargon—they are fundamental to the efficiency and scalability of AI systems. As the field matures, innovation in data types will be critical to balancing accuracy, performance, and power consumption.

Whether you’re building models for the data center or deploying AI at the edge, understanding and leveraging the right data type is an important skill. Keep an eye on this space.