DeepSeek V4 has been officially released, targeting advanced scenarios such as long-document understanding, code generation, complex task planning, enterprise knowledge bases, and industrial AI agents. The new model introduces higher requirements for “chip–model synergy,” spanning AI computing power, inference frameworks, memory scheduling, multi-card parallelism, KV cache management, and operator optimization.

At the same time, Huawei announced that its full lineup of Ascend hyper-node products now fully supports the DeepSeek V4 model series. Leveraging advanced optimization technologies—including high-performance fused operators, asynchronous scheduling frameworks, Multi-Token Prediction (MTP), and long-context management—the Ascend CANN (Compute Architecture for Neural Networks) ecosystem delivers high-performance inference for DeepSeek V4’s native 1M-token context capability.

As an Ascend Diamond Partner, Colorlight has taken the lead in deploying and scheduling DeepSeek V4 on the Ascend Atlas 800 A3 Super-Node platform.

Designed for MoE (Mixture-of-Experts) models and ultra-long-context inference scenarios, Colorlight’s self-developed computing power scheduling platform enables system-level scheduling and task management across multiple cards and nodes. Built on the Atlas 800 A3 Super-Node architecture, it delivers advanced large-model scheduling capabilities for high-performance AI workloads.

This solution has already been successfully deployed in the world’s first Huawei 384 Super-Node project for scientific research and education, providing valuable real-world experience for the stable operation of domestic large models on ultra-large-scale computing clusters.

Focusing on the large-scale deployment of domestic models such as DeepSeek V4, Colorlight is building a comprehensive AI inference product matrix. The full-stack portfolio includes AI inference modules, inference cards, multi-card servers, super-node scheduling platforms, and industry-specific AI agent all-in-one systems—covering a wide range of scenarios from edge inference and private deployment to large-scale cluster scheduling.

The large model industry is now shifting from “parameter competition” to “engineering deployment competition.” For enterprise customers, the real value lies not in the model itself, but in its ability to run reliably on-site, integrate seamlessly with business systems, ensure data security, support continuous optimization, and ultimately be delivered as scalable, replicable industry solutions.

Working closely with the Huawei ecosystem, Colorlight leverages its strengths in ultra-node computing power scheduling, operator optimization, and model adaptation, along with deep expertise in display control, edge devices, AI hardware productization, and industry applications. Together, these capabilities are driving the real-world deployment of leading domestic large models—including DeepSeek, Qwen, GLM, and MiniMax—across sectors such as government and enterprise, education, conferencing, security, and display control.

Looking ahead, Colorlight will continue to build on the Huawei ecosystem, focusing on the integration of “domestic large models + domestic AI computing power + industry-ready productization.” By continuously investing in a comprehensive product portfolio, the company is committed to helping customers build secure, controllable, and efficient domestic AI infrastructure—accelerating the adoption of large models across industries.