Fast inference API served from wafer-scale AI hardware
Cerebras builds wafer-scale chips and sells access to them as an inference service. The pitch is throughput: by keeping model weights on a very large piece of silicon, the hardware avoids much of the memory movement that slows conventional GPU serving, which shows up as high tokens per second and low latency on open-weight models. Developers reach it through an OpenAI-compatible API, so existing clients often need only a base URL and key change, and a free tier exists for prototyping. The model catalogue is narrower than a general cloud provider's.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
high throughput inference, latency sensitive chat, agent workloads and benchmark testing
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The chip keeps model weights on a very large wafer, so inference avoids shuttling data between memory and processors as often. That shows up as high throughput and low latency on supported models, though it does not change model quality.
The catalogue focuses on popular open-weight models rather than every model on the market, and it grows as new releases are optimised for the hardware. Check the current list before assuming a specific family is available.
It is aimed at evaluation and light development, with rate limits and shared capacity. It is enough to benchmark latency with your own prompts, but production traffic needs a paid arrangement with committed capacity.
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