Oct 22 2026
How we deploy Trillion-parameter frontier models on Cerebras
Sarah Chieng
One of the most frequently asked questions we get is how we deploy large models on Cerebras wafer-scale chips. A single Cerebras WSE-3T wafer has 44 GB of on-chip memory, and trillion-parameter frontier models
need terabytes for their weights alone.
In this post, we’ll explain how we split a large frontier model across multiple
WSE-3T
wafers, to run multi-trillion parameter models at 1000-2000+ tokens per second.
We’ll use the open source model Kimi K3, with ~3 trillion parameters, as an illustrative example.
By the end, you'll understand:
How we fit the full model into on-chip memory
How we map the model across multiple chips
How we handle a high # of concurrent requests
Breaking it down with Kimi K3
Kim (EN)

---
**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://www.cerebras.ai/blog/how-we-deploy-trillion-parameter-frontier-models-on-cerebras)获取最准确的信息。

---
🔗 **原文链接**: [Cerebras Deploys Trillion-Parameter AI Models](https://www.cerebras.ai/blog/how-we-deploy-trillion-parameter-frontier-models-on-cerebras)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 55票 · 👤 gmays

---
🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

---
⏱️ 2026-10-25 13:01