Core Insights - The first MUSA Developer Conference (MDC 2025) was held by Moore Threads in Beijing, where the company unveiled its new GPU architecture "Huagang" and a series of technological advancements [2] - Moore Threads has established a complete technology stack based on its self-developed unified architecture, covering "chip-edge-end-cloud" integration, and plans to increase R&D investment [2] Group 1: New Architecture and Chip Roadmap - The MUSA (Meta-computing Unified System Architecture) has been upgraded to version 5.0, achieving key breakthroughs in full-stack unification, performance, and ecological openness [3] - The "Huagang" architecture supports full precision calculations from FP4 to FP64, with a 50% increase in computing density and a 10-fold improvement in energy efficiency, capable of supporting over 100,000 card-scale intelligent computing clusters [3] - Two upcoming chip technologies based on the "Huagang" architecture were announced: "Huashan," focusing on AI training and ultra-large-scale intelligent computing, and "Lushan," specializing in high-performance graphics rendering [3][5] Group 2: AI Training and Computing Clusters - The newly launched "Kua'e" intelligent computing cluster achieves full precision and general computing capabilities, with a floating-point computing capacity of 10 Exa-Flops and training efficiency rates of 60% for Dense models and 40% for MOE models [7] - The MTT S5000 single card has achieved breakthroughs in inference performance, with a throughput of over 4000 tokens/s for Prefill and 1000 tokens/s for Decode [7] - Future architecture planning for the MTT C256 super node aims to enhance training efficiency and inference capabilities for large-scale intelligent computing centers [7] Group 3: Graphics Computing and AI Technologies - Moore Threads' products support major graphics and computing APIs, including DirectX 12 and Vulkan 1.3, and have achieved compatibility with mainstream domestic CPUs and operating systems [8] - Key breakthroughs in rendering technology include hardware-level ray tracing acceleration and self-developed AI generative rendering technology, enabling realistic lighting effects on domestic GPUs [8] - The MT Lambda embodiment intelligence simulation training platform integrates physics, rendering, and AI engines for efficient development and training environments [8] Group 4: Ecosystem Development and Education - The concept of "ecosystem" was emphasized, with the company focusing on building a self-reliant domestic computing industry ecosystem through collaboration and innovation [11] - The company has established a developer growth system through the Moore Academy, gathering nearly 200,000 developers and learners, and engaging over 100,000 students in over 200 universities [11] - The company plans to open-source key simulation acceleration components to enhance research and development efficiency in the robotics industry [9]
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