Cerebras

What this post added

This post details the training and inference of the Jais-2 Arabic-centric LLMs on Cerebras wafer-scale clusters. It highlights the use of a unified MemoryX and SwarmX fabric for efficient training, where all model parameters are streamed from a single, terabyte-scale block of memory, eliminating the need for complex parallelism strategies. For inference, model weights are loaded directly into on-wafer SRAM across interconnected CS-3 systems, achieving petabyte-per-second bandwidth and enabling an output speed of 2,000 tokens per second for the Jais-2 70B model, which is over 20x faster than comparable GPU-based systems. The post also outlines the five key stages of development for Jais-2: model design and scaling-law exploration, pretraining, supervised fine-tuning, and alignment (DPO and GRPO).