Cerebras Deploys Trillion-Parameter AI Models
Cerebras describes how it deploys a roughly 3-trillion-parameter open model, Kimi K3, across multiple WSE-3T wafers with 44 GB of on-chip memory each. The post explains splitting weights at 4-bit precision into per-layer chunks of about 16 GB, placing one dense-attention layer per wafer with room for its KV cache, and fitting two linear-attention layers per wafer. Wafers form a pipeline passing activations over RDMA, with many requests kept in flight to keep stages busy. The method is presented as an illustrative calculation.
Open original source www.cerebras.aiOne primary source; other links are context, not independent corroboration.
- Primary sourceHacker News · AI
- Discovery linkHacker News
- Linked in sourceWSE-3T
- Linked in sourcean impressive 88.3% on Terminal-Bench 2.1
- Linked in sourceRead more about Kimi K3 here
- Linked in sourceHugging Face implementation
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Cerebras describes how it deploys a roughly 3-trillion-parameter open model, Kimi K3, across multiple WSE-3T wafers with 44 GB of on-chip memory each. The post explains splitting weights at 4-bit precision into per-layer chunks of about 16 GB, placing one dense-attention layer per wafer with room for its KV cache, and fitting two linear-attention layers per wafer. Wafers form a pipeline passing activations over RDMA, with many requests kept in flight to keep stages busy. The method is presented as an illustrative calculation.
Key findings from the stored brief
- Kimi K3 is described as a 3T-parameter model with 69 linear-attention layers and 24 dense layers.
- The plan maps 35 wafers for linear layers plus 24 for dense layers, totaling 59 wafers.
- Activations pass between wafers over RDMA over Ethernet at roughly 2 microseconds per hop.
- Concurrent requests are limited by on-chip memory left for KV cache after each dense layer.
The calculation uses assumed values such as a 256k average request size and 4-bit quantization, and the post presents it as illustrative; source claims not independently checked.
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Why this may matter
It offers a concrete, arithmetic-level account of how trillion-parameter models can be served on wafer-scale hardware, relevant to inference cost and latency decisions for large open models.
Limits and open questions
The calculation uses assumed values such as a 256k average request size and 4-bit quantization, and the post presents it as illustrative; source claims not independently checked.
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