HomeHygon DCU Runs Trillion-Parameter Models With Kimi K3 Expert Parallelism
Domestic computing infrastructure has successfully powered a trillion-parameter model for the first time. Hai Guang Information confirmed full-stack adaptation and performance validation of Kimi K3 on its DCU platform, demonstrating that executing such large-scale models is no longer restricted to a select few foreign flagship chips.
By optimizing everything from underlying operators to the inference engine, Hai Guang DCU enables model code to run directly without modification, effectively removing migration barriers. Developers can deploy models immediately upon acquiring resources, ensuring seamless compatibility. Custom operator-level adjustments for the KDA attention mechanism and the 896-expert MoE architecture allow for efficient, stable inference even under heavy loads with millions of context tokens.
In real-world applications, Kimi K3 on Hai Guang DCU supports advanced use cases like long-range intelligent agents, visual closed-loop generation, and self-designed chip integration. Domestic computing users can now independently run open-source trillion-parameter models. Notably, prior to K3’s release, Yueling Dark Face partnered with Hai Guang and CAS Sustech to achieve full compatibility optimization. Leveraging the DCU hardware stack, Sustech integrated distributed training and multi-card parallel inference for the Kimi MoE architecture, while Hai Guang DCU ensured stable long-text inference on the Sustech 8000 cluster through deep hardware-software collaboration.
Official benchmarks confirm that performance loss on domestic cards remains under 12%, meeting commercial deployment standards. As the gap between trillion-parameter models and domestic GPUs narrows, the computing power landscape of the intelligent era is quietly transforming.
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Domestic computing infrastructure has successfully powered a trillion-parameter model for the first time. Hai Guang Information confirmed full-stack adaptation and performance validation of Kimi K3 on its DCU platform, demonstrating that executing such large-scale models is no longer restricted to a select few foreign flagship chips.
By optimizing everything from underlying operators to the inference engine, Hai Guang DCU enables model code to run directly without modification, effectively removing migration barriers. Developers can deploy models immediately upon acquiring resources, ensuring seamless compatibility. Custom operator-level adjustments for the KDA attention mechanism and the 896-expert MoE architecture allow for efficient, stable inference even under heavy loads with millions of context tokens.
In real-world applications, Kimi K3 on Hai Guang DCU supports advanced use cases like long-range intelligent agents, visual closed-loop generation, and self-designed chip integration. Domestic computing users can now independently run open-source trillion-parameter models. Notably, prior to K3’s release, Yueling Dark Face partnered with Hai Guang and CAS Sustech to achieve full compatibility optimization. Leveraging the DCU hardware stack, Sustech integrated distributed training and multi-card parallel inference for the Kimi MoE architecture, while Hai Guang DCU ensured stable long-text inference on the Sustech 8000 cluster through deep hardware-software collaboration.
Official benchmarks confirm that performance loss on domestic cards remains under 12%, meeting commercial deployment standards. As the gap between trillion-parameter models and domestic GPUs narrows, the computing power landscape of the intelligent era is quietly transforming.
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