© 2026 AI-Buzz. Early access — data updated daily.
© 2026 AI-Buzz. Early access — data updated daily.
AI compute company with wafer-scale chips
Founded by: Andrew Feldman, Gary Lauterbach, Sean Lie, Michael James
Metrics computed from HN discussion, GitHub activity, and funding data.
According to AI-Buzz, Cerebras with 37% positive developer sentiment (144 HN comments analyzed), with 1,233,299 PyPI downloads in 30 days.
Source: https://ai-buzz.com/companies/cerebras?utm_source=citation&utm_medium=referral&utm_campaign=cite_this_data
Metrics derived from public APIs (HN Algolia, GitHub, npm/PyPI). Sentiment classified by AI. See methodology for details →
Description
AI compute company with wafer-scale chips
Estimated Company Size
500 - 1000 employees
Website
cerebras.aiFounded
2016
Description
Cerebras Systems is an AI compute company that designs and builds specialized hardware and software for accelerating deep learning workloads. Their flagship product, the CS-2 system, features the Wafer-Scale Engine (WSE), the world's largest AI chip, enabling unprecedented performance for training large AI models. They aim to overcome traditional compute limitations, making them a significant player in the high-performance AI infrastructure ecosystem.
Community engagement metrics that indicate developer traction and interest.
Last updated: 1 day ago
Mentions in HN discussions. Source: Hacker News Algolia API.
Sentiment analysis of Hacker News comments only. Does not include Reddit, Discord, or other platforms.
Package download volume indicates real-world adoption and integration into production projects.
A complete AI supercomputer designed for deep learning, featuring the Wafer-Scale Engine 2 (WSE-2) for unparalleled compute performance.
The world's largest and fastest AI chip, designed to accelerate deep learning workloads with massive on-chip memory and bandwidth.
A comprehensive software stack that optimizes AI model training and inference on Cerebras hardware, supporting popular frameworks like PyTorch and TensorFlow.
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Package downloads and ecosystem metrics — 30-day window
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