The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

EFloat: Entropy-coded Floating Point Format for Deep Learning

We describe the EFloat floating-point number format with 4 to 6 additional bits of precision and a wider exponent range than the existing floating point (FP) formats of any width including FP32, BFloat16, IEEE-Half precision, DLFloat, TensorFloat, and 8-bit floats. In a large class of deep learning models we observe that FP exponent values tend to cluster around few unique values which presents entropy encoding opportunities. The EFloat format encodes frequent exponent values and signs with Huffman codes to minimize the average exponent field width. Saved bits then become available to the mantissa increasing the EFloat numeric precision on average by 4 to 6 bits compared to other FP formats of equal width. The proposed encoding concept may be beneficial to low-precision formats including 8-bit floats. Training deep learning models with low precision arithmetic is challenging. EFloat, with its increased precision may provide an opportunity for those tasks as well. We currently use the EFloat format for compressing and saving memory used in large NLP deep learning models. A potential hardware implementation for improving PCIe and memory bandwidth limitations of AI accelerators is also discussed.