Cerebras Deploys Trillion-Parameter AI Models
Cerebras Systems details the technical process of deploying the 3-trillion parameter Kimi K3 AI model across its wafer-scale WSE-3T chips. The approach uses a pipeline architecture to distribute model layers across multiple wafers to achieve high-speed token generation.
Why it matters
This demonstrates a significant advancement in hardware-software co-design for running massive frontier AI models efficiently.
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.
Kimi K3 is an open source, 3T parameter model with 69 linear-attention layers and 24 dense layers.
It scores an impressive 88.3% on Terminal-Bench 2.1 (vs. GPT-5.6 Sol's 88.8%) and 91.2% on BrowseComp (vs. GPT-5.6 Sol's 90.4%). These results demonstrate frontier-level coding and agentic capabilities, even outperforming leading proprietary models on some benchmarks.
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