Cerebras provides a wafer‑scale AI compute platform that runs inference, fine‑tuning, and full‑parameter training of large language models on a single engine, delivering up to 3,000 tokens per second and reducing total cost of ownership versus GPU clusters. The system is offered as on‑premise CS‑2/CS‑3 hardware, private‑cloud capacity, or a pay‑as‑you‑go SaaS, with a drop‑in OpenAI‑compatible API and SOC 2/HIPAA‑certified data handling for enterprise workloads.
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.
+7Founders
Product
Problem
Enterprises and developers face high latency, prohibitive cost, and scaling limits when running large language models on conventional GPU clusters, making real‑time inference, extensive fine‑tuning, and full‑parameter training impractical for many workloads.
Solution
Cerebras delivers AI infrastructure built around its Wafer‑Scale Engine (WSE) hardware, available as on‑premise systems (CS‑2, CS‑3), dedicated private‑cloud capacity, or a pay‑as‑you‑go cloud service. The platform provides world‑record inference speeds (up to 3,000 tokens / s) and enables training and fine‑tuning of frontier models without the fragmentation typical of GPU farms. A drop‑in OpenAI‑compatible API lets users migrate existing workloads instantly, while SOC 2/HIPAA‑certified data handling ensures enterprise compliance. By consolidating inference, fine‑tuning, and training on a single engine, Cerebras reduces total cost of ownership and eliminates the need for multi‑step pipelines.
Target Audience
The primary customers are enterprise AI teams, cloud‑native developers, and research organizations that require ultra‑low latency, high‑throughput inference or large‑scale model training, such as fintech, pharma, autonomous systems, and generative‑AI product companies.
Features
- Wafer‑Scale Engine (WSE‑2/3) delivering >4 exaflops FP16 performance and up to 30× faster inference than leading GPU clouds.
- Native support for full‑parameter models up to 120 B + parameters (e.g., GPT‑OSS‑120B, Llama 3, Qwen 3) with token throughput of 1,000‑3,000 tokens / s.
- Unified platform for inference, fine‑tuning, and pre‑training on the same hardware, eliminating data movement overhead.
- Drop‑in OpenAI API compatibility and SDKs for seamless integration into existing applications.
- Deployment flexibility: on‑premise CS‑2/CS‑3 clusters, dedicated private‑cloud capacity, or cloud‑native SaaS with instant provisioning.
- Enterprise‑grade security: SOC 2, HIPAA certifications, end‑to‑end encryption, and role‑based access controls.
- Integrated pricing model with per‑token rates (e.g., $0.35 / M tokens for GPT‑OSS‑120B inference) and tiered subscription plans (Free, Developer, Enterprise).
- Partner integrations for easy consumption via OpenRouter, Hugging Face Hub, and Vercel AI Gateway.