Challenges
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Valuable knowledge assets, but no clear path to model-ready structuring
Manufacturing enterprises hold extensive domain knowledge, including process parameters and anomaly judgment rules. However, this knowledge is largely unstructured and scattered across documents and individual expert experience, making it difficult to directly leverage for model training. As a result, AI initiatives often depend on a small number of experts, and valuable knowledge cannot be reused or scaled effectively.
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General-purpose models fall short in deep industrial scenarios
In industrial environments with strict process constraints, general-purpose large models often exhibit gaps in understanding and loss of critical detail. While enterprises need to build proprietary model systems tailored to their processes, most lack experience in large-scale model training and systematic optimization, limiting practical adoption.
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Compute investment does not translate into engineering efficiency
As data security and intellectual property requirements increase, more manufacturers are building on-premises GPU clusters. However, mismatches between hardware configurations and model scale, along with suboptimal distributed strategies, lead to low cluster utilization. At the same time, traditional IT architectures struggle to support AI-centric system engineering, constraining overall training efficiency.
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High requirements for private deployment amid growing inference complexity
With strict data-locality requirements, enterprises must deploy models on-premises. Meanwhile, modern inference systems have evolved into complex architectures involving high-concurrency scheduling, dynamic scaling, and MoE optimization. This significantly raises the bar for system tuning and operational expertise within manufacturing organizations.
Building an Intelligent Productivity Foundation for Manufacturing
INFINIGENCE's Intelligent manufacturing industry solution，addresses structural challenges in manufacturing transformation, including fragmented infrastructure, mismatched model capabilities, inefficient data flow, and complex private deployment. Through deep integration of training system architecture, inference engine capabilities, cluster resource management, and industrial vertical domain capability ecosystem, we provides an integrated intelligent service—including intelligent infrastructure management, model training optimization, inference deployment and scheduling, and industry ecosystem collaboration. This helps enterprises complete a full-chain close-loop system—from vertical domain knowledge accumulation to large-scale model deployment—while ensuring data security and business continuity. Our solution supports the upgrading of core business scenarios such as R&D, manufacturing, operations, and services, building a long-term, evolvable productivity foundation for manufacturing enterprises. The INFINIGENCE Smart Manufacturing Solution is designed to address the structural challenges encountered during the digital and intelligent transformation of manufacturing enterprises, including fragmented infrastructure, misaligned model capabilities, inefficient data flow, and the complexity of private deployment. By deeply integrating training system architecture, inference engine capabilities, cluster-level resource management, and an ecosystem of industrial domain expertise, the solution delivers an end-to-end intelligent service spanning infrastructure management, model training optimization, inference deployment and scheduling, and industry ecosystem collaboration. This enables enterprises to build a closed-loop pipeline—from domain knowledge consolidation to large-scale model deployment—while ensuring data security and business continuity. The solution supports continuous upgrades across core business scenarios such as R&D, manufacturing, operations, and after-sales services, forming a long-term, evolvable productivity foundation for the manufacturing industry.
Advantages
More unified compute scheduling, more systematic model training
More efficient inference, and deeper integration with industrial domain ecosystems
Unified Accelerator Scheduling and Cluster Management
A comprehensive capability stack covering heterogeneous accelerator support, unified multi-cluster scheduling, topology-aware scheduling, and system-level performance validation.
Unified management and scheduling across diverse heterogeneous accelerators
Stable operation at scales of thousands of accelerators and beyond
Systematic cluster-level performance testing and diagnostic capabilities
System-Level Training Optimization for Ultra-Large Models
An integrated training framework spanning memory management, parallel strategy design, communication optimization, pipeline scheduling, and system stability control.
Not limited to isolated parameter-level tuning
Emphasizes co-design of model architecture and cluster topology
Supports large-scale MoE and multi-modal model training
Inference Systems for Ultra-Large Models with Complex Architectures
A comprehensive inference engine framework addressing long-context optimization, resource-isolated scheduling, expert-parallel scaling, and cluster-level elasticity.
Stage-aware optimization for prefill and decode phases
Scalable scheduling for MoE architectures
Stable, production-grade serving for multiple business systems
Industrial Domain Ecosystem Synergy
Deep integration of domain knowledge modeling with industry-specific solutions through an open and collaborative ecosystem.
Alignment between domain knowledge modeling and model training pipelines
Seamless interaction between industry applications and inference platforms
Unified coordination between knowledge foundations and compute infrastructure
Value
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Accelerating the Scaled Adoption of Industrial Intelligence
Through system‑level training optimization, high‑performance inference architectures, and unified scheduling of heterogeneous compute resources, we establish a complete engineering pipeline from knowledge modeling to cluster‑level services. This enables enterprises to overcome compute capacity and system stability constraints when scaling AI applications across factories and production lines.
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Building a Sustainable and Predictable Compute Cost Structure
By optimizing training and inference performance at the system level, enabling unified scheduling across heterogeneous accelerators, and supporting elastic scaling, GPU utilization is significantly improved while reducing wasted compute consumption. Manufacturing enterprises can shift from coarse resource usage to refined, cost-efficient compute operations.
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Ensuring Industrial-Grade Business Continuity And System Stability
Comprehensive stability mechanisms are built across distributed training fault tolerance, multi-cluster active-active scheduling, system-level communication optimization, and end-to-end performance monitoring. These capabilities effectively mitigate the risks of task interruptions and performance volatility in production environments.
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Turning Industrial Knowledge into Reusable Intelligent Assets
By establishing industry-specific knowledge models and data foundations, supporting domain model training and continuous fine-tuning, and building enterprise-level knowledge engines and agent systems, expert tacit knowledge is transformed into structured, reusable digital assets.
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Enabling Autonomous and Controllable Intelligent Infrastructure
With support for private deployment, multi-accelerator compatibility, and scalability to clusters of tens of thousands of accelerators, enterprises can build and operate AI model systems within fully controlled internal environments. Continuous architectural optimization and ecosystem collaboration ensure that intelligent infrastructure remains adaptable to future technological evolution.
Use Case
INFINIGENCE & CXMT: Overcoming Training Memory Bottlenecks and Optimizing Long-Context Inference for Semiconductor Domain Models
To meet CXMT’s requirements for semiconductor domain-specific large models, INFINIGENCE delivered system-level optimizations across both training and inference workflows. On the training side, coordinated memory optimization techniques and parallel strategy design addressed GPU memory bottlenecks associated with ultra-long-context model training, enabling stable training of proprietary models for circuit design, routing, and related workloads. On the inference side, long-context inference optimizations effectively mitigated performance degradation under long-sequence inputs, allowing the model to maintain efficient and stable inference output when handling complex accelerator design tasks.
Infinite Computing, Accelerating the Future of AGI
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