Cerebras AI Inference and Training Platform
Cerebras AI Inference and Training Platform
Introduction to the Cerebras Platform
The Cerebras platform combines specialized hardware, software, and cloud services so you can develop and deploy AI models with less complexity. At its core is a wafer-scale engine housed in purpose-built systems (like the CS-series), coupled with a software stack that abstracts away parallelism and memory management. On top of this, Cerebras offers cloud products for both training and inference, giving you options to experiment, scale, and serve models without owning the hardware.
Cerebras Systems
Cerebras Systems is a semiconductor and AI company focused on accelerating deep learning.
It is known for creating the world’s largest single chip for AI workloads (the Wafer Scale Engine) and building turnkey AI systems and supercomputers around it.
The company’s mission is to dramatically reduce the time, cost, and complexity of training and deploying state-of-the-art AI models.
Inference Cloud
You can bring your own models or use available, optimized models, then set rate limits, monitor latency, and scale up as traffic grows.
It’s designed to keep per-request costs predictable while maintaining consistent response times.
- What Is AI Inference?
- AI inference is when a trained model is used to make predictions or generate outputs for new inputs. Think of it as running the model “in production” to answer questions, write text, classify images, or recommend items. The goals are low latency, high throughput, and cost efficiency while preserving model quality.
Training Cloud
The Training Cloud offers access to Cerebras hardware and tooling for pretraining and fine-tuning. It abstracts distributed compute details—such as model/tensor parallelism—so you can focus on data, objectives, and evaluation. This helps teams iterate faster on experiments and move promising models to production sooner.
- What Is AI Training?
- AI Training is the process of teaching a model by showing it lots of data and adjusting its internal parameters to reduce error. It typically requires heavy compute and careful tuning of data pipelines, optimizers, and hyperparameters. The outcome is a model that has learned patterns it can later apply during inference.
CS-3 System
CS-3 System is a purpose-built AI system that integrates compute, memory, networking, and cooling around the latest wafer-scale engine. By minimizing the need to split models across many small devices, CS-3 aims to reduce training complexity and synchronization overhead. The result is a more straightforward path to high performance on large models.
AI Supercomputers
Cerebras AI supercomputers combine multiple CS-series systems into a cohesive cluster engineered for AI workloads. They are designed for multi-trillion-parameter-class training runs and high-throughput inference. The infrastructure, software, and scheduling are tuned end-to-end around the needs of deep learning at scale.
Wafer Scale Engine
The Wafer Scale Engine is a single silicon wafer transformed into one massive chip, hosting an enormous number of compute cores and on-chip memory. Its architecture provides high bandwidth and low latency for AI operators, reducing the communication penalties common in multi-chip systems. This unique design underpins Cerebras’ performance and simplicity advantages.
More information:
- https://www.cerebras.ai/company
