Cerebras Systems provides a wafer‑scale AI processor that offers vastly higher memory bandwidth and lower latency than traditional GPUs, allowing developers to train and serve models from 1 B to 24 T parameters without sharding or code changes. The platform is available via cloud, private‑cloud API, or on‑premise deployment with OpenAI‑compatible endpoints and usage‑based pricing.
Funding
$1.1B raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
6OAM+1Founders
Product
Problem
AI developers and enterprises face latency and scalability limits with conventional GPU‑based inference and training, which slow down real‑time applications, iterative model development, and large‑scale deployments.
Solution
Cerebras Systems offers a wafer‑scale AI processor (the Cerebras Wafer‑Scale Engine) that delivers orders‑of‑magnitude higher memory bandwidth and lower latency than GPUs, enabling ultra‑fast inference and seamless training of models from 1 B to 24 T parameters. The platform is available as a cloud service, private‑cloud API, or on‑premise deployment, providing drop‑in OpenAI‑compatible APIs and built‑in networking for disaggregated inference (prefill on AWS Trainium, decode on Cerebras CS‑3). Users can train, fine‑tune, and serve models without sharding or code rewrites, while retaining full data ownership. Pricing is transparent and usage‑based, with free, developer, and enterprise tiers that scale from per‑token to per‑hour billing.
Target Audience
Primary customers are AI developers, data science teams, and enterprise engineering groups that require low‑latency inference and large‑scale model training for real‑time applications, generative AI services, and research workloads.
Features
- Wafer‑scale engine with thousands of times greater memory bandwidth than the fastest GPU, eliminating model parallelism and sharding.
- Disaggregated inference architecture (prefill on Trainium, decode on CS‑3) for optimal token‑per‑second throughput.
- Unified cloud, private‑cloud, and on‑premise deployment options with OpenAI‑compatible API endpoints.
- Automatic scaling and preconfigured environments that start training in minutes without DevOps effort.
- Full data and model ownership; no logging or reuse of customer data unless explicitly authorized.
- Support for a wide range of model architectures, including dense, MoE, multimodal, and agentic models, with built‑in libraries for Llama, Mistral, GLM, and diffusion models.
- Transparent usage‑based pricing (free tier, developer tier starting at $10, enterprise tier with dedicated capacity and custom model support).