💡Cerebras WSE-3T Supports Kimi K3 With 59 Wafers
Kimi K3 Needs 59 Cerebras Wafers to Run
TL;DR
Cerebras WSE-3T wafer-scale system supports Kimi K3 with 44 GB of on-chip memory per wafer. Each wafer handles 16 GB per layer, with 59 wafers needed for Kimi K3. This setup ensures ultrafast latency and high throughput.
Cerebras WSE-3T wafer-scale system now supports Kimi K3, a 3 trillion-parameter model. Each wafer has 44 GB of on-chip memory, crucial for handling Kimi K3's 16 GB per layer requirement. This setup allows for ultrafast latency and high throughput, with activations taking roughly 2 microseconds per hop. Developers working with large models will see significant improvements in inference speed and efficiency. Each layer in Kimi K3 needs about 16 GB of memory, with 59 Cerebras WSE-3T wafers required to support the model.
Key Points
Cerebras WSE-3T wafer has 44 GB of on-chip memory.
Kimi K3 model has 3 trillion parameters and 69 linear-attention layers.
Kimi K3 scores 88.3% on Terminal-Bench 2.1 and 91.2% on BrowseComp.
Each layer in Kimi K3 needs about 16 GB of memory.
Cerebras WSE-3T executes models in a pipeline, with activations passing via RDMA over Ethernet.
Why It Matters
If you're working with ultra-large models like Kimi K3, Cerebras WSE-3T's wafer-scale architecture can significantly boost inference speed and efficiency. Each wafer's 44 GB of on-chip memory supports 16 GB per layer, with 59 wafers needed for Kimi K3. This setup ensures ultrafast latency and high throughput, making it a game-changer for teams pushing the limits of AI.
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