OpenAI's Cerebras chip is quite interesting 😅 1. Wafer-Scale: The world's largest chip, as big as a face! 😅 Transistor Count: 4 trillion transistors (for comparison, the H100 only has 80 billion). 2. Extremely High On-Chip SRAM Bandwidth This is Cerebras' core weapon that allows it to outperform GPUs. Eliminating Bottlenecks: In traditional GPU architectures, model calculations require frequent data transfer between video memory (HBM) and computing cores, resulting in huge energy consumption and latency. (Memory computing dilutes the growth rate of HBM, but the market is large enough, and SRAM is also expensive, so it doesn't pose a significant threat to the three giants, Samsung, Hynix, and Micron, in the early stages.) Full On-Chip Storage: Cerebras boasts up to 44GB of on-chip SRAM memory, with a bandwidth of 21 PB (PetaBytes) per second. This means that most of the model's weights can be stored entirely on the chip, with read/write speeds thousands of times faster than GPU memory, enabling "instantaneous" inference like OpenAI models. 3. Extremely Simple Programming and Expansion Single Machine as Cluster: Due to the chip's large size, the computing power of a single Cerebras node (CS-3) is equivalent to dozens or even hundreds of traditional GPU nodes. No Need to Split the Model: Developers don't need to split a large model into many parts and consider complex cross-server communication (model parallelism) as on GPU clusters. From Cerebras' perspective, the entire model runs on a single chip. 4. Sparse Optimization for Large Language Models (LLM) Handling Zero Values: Many weights in AI models are "zero" (sparseness), and traditional GPUs still perform ineffective calculations on these zeros. The Cerebras chip has a built-in sparse computing engine that can directly skip zero values, thereby further extracting performance.