How to Run Trillion-Parameter Models on Cerebras Chips

At 4-bit precision, Kimi K3 carries roughly 1.5 terabytes of model weights. Meanwhile, a single Cerebras WSE-3T wafer holds just 44 gigabytes of on-chip memory. Yet Cerebras aims to serve this 3-trillion-parameter giant at over 1,000 tokens per second. A simple division (1.5 TB divided by 44 GB) suggests you need 34 wafers. But in reality, Cerebras deploys it across 59 wafers. How does a model that massive run on chips that small without grinding to a halt? In this breakdown, we walk through the exact layer-by-layer arithmetic, why weights never move across the network, and the architectural memory bottleneck that determines how many users can chat at once. 🔗 Sources & Documentation: • Cerebras Engineering Blog - Deploying Trillion-Parameter Models: https://www.cerebras.ai/blog/how-we-deploy-trillion-parameter-frontier-models-on-cerebras • Kimi K3 Model Card & Architecture Specs: https://huggingface.co/moonshotai/Kimi-K3 • The Register - Cerebras WSE-3T Hardware Specifications: https://www.theregister.com/a/5289286 • Cerebras Blog - Kimi K2.6 Enterprise 981 Tokens/sec Trial: https://www.cerebras.ai/blog/cerebras-kimi-k2-Enterprise • Artificial Analysis - Kimi Model Speed Benchmarks: https://artificialanalysis.ai/models/kimi-k3 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/mhgXXmhrh #ai #cerebras #hardware #machinelearning #semiconductors #deeplearning #cloudcodes