In the current flourishing development of artificial intelligence, the landing of large model applications puts forward stringent requirements for computing resources. The traditional GPU solution faces problems such as high power consumption, low efficiency, and complex deployment, becoming a bottleneck restricting the scaled development of the AI industry. Our flagship large model inference card is born, with a groundbreaking technical architecture and excellent performance indicators, redefining the AI computing standard, providing core driving force for enterprises to reduce costs and increase efficiency, and unleashing intelligent computing potential.
Core Value Positioning of the Solution
This inference card is positioned with "high energy efficiency, strong performance, and easy deployment" as the core, targeting "RTX5090" level products, achieving comprehensive breakthroughs in model processing, energy consumption control, cluster expansion, and other dimensions. Through the self-developed LPU architecture and innovative technology combination, it not only meets the high computing power requirements for large model inference but also helps enterprises build a green and efficient AI computing infrastructure with its low power consumption and high compatibility advantages, promoting the large-scale landing of AI applications from concept.
Core Performance Indicator Analysis
1. Super Strong Model Processing Capability
The inference card is equipped with a model with 32 billion parameters, possessing powerful knowledge carrying and processing capabilities, and efficiently supporting complex AI tasks such as natural language processing and image recognition. The token throughput rate is ≥2000 tokens/minute, ensuring the fluency and real-time performance of large model inference, and meeting the needs of high-frequency interaction scenarios such as online Q&A and intelligent customer service. A single card supports 8-way inference stream for optimal concurrent performance, significantly improving task processing efficiency per unit computing power and reducing enterprise computing resource investment costs.
2. Ultimate Energy Efficiency Breakthrough
The nominal power consumption is only ≈120W, compared to the 250-300W power consumption of mainstream GPU products, achieving over 50% energy consumption reduction. This breakthrough significantly reduces the electricity and cooling costs of data centers, helps enterprises practice the concept of green and low-carbon development, and builds a sustainable AI infrastructure while reducing operating expenses.
Five Technical Advantages Empowering Intelligent Computing Upgrade
1. Dedicated LPU Architecture Innovation
Abandoning the general GPU design, the self-developed Large-language-model Processing Unit (LPU) architecture is deeply optimized for the sparse computation, low-precision operation, and attention mechanism of Transformer-like models. Through customized hardware design, the model inference efficiency is greatly improved, and the performance in large model tasks can be increased by several times compared to the traditional architecture.
2. High Computing Power Density Design
Using dedicated high-speed processing units and High Bandwidth Memory (HBM) technology, data movement and memory access delays are minimized to the greatest extent, constructing an efficient data transmission link. In large-scale data processing scenarios, it ensures rapid data circulation and computation, avoiding computing power waste caused by data transmission bottlenecks.
3. Flexible and Scalable Cluster Solution
Supporting multi-chip interconnection technology, achieving near-linear expansion of computing power. Enterprises can flexibly increase the number of inference cards according to business development needs, easily building large-scale AI computing clusters. Whether it's the initial deployment of small and medium-sized enterprises or the complex scenario applications of large institutions, seamless adaptation is possible, ensuring on-demand allocation and efficient utilization of computing power resources.
4. Low Power Consumption Hybrid Precision Technology
Natively supports dynamic precision scheduling such as INT2/INT4/INT8, intelligently switches computing precision according to task requirements, further improving performance/power consumption ratio while ensuring the precision of model inference. In preprocessing, initial inference, and other links with lower precision requirements, low-precision computing is used to greatly reduce energy consumption; at key nodes, high-precision computing is automatically switched to ensure result accuracy.
5. Full Ecosystem Compatibility Design
With high compatibility, it can connect to mainstream AI model frameworks and various middleware products. The deployment process is simple and fast, without the need for large-scale transformation of the existing AI development environment, and the latency is controllable, helping enterprises quickly integrate the inference card into the existing AI computing system, accelerating application development and landing process.
Our flagship large model inference card provides enterprises with a full-scenario AI computing solution from single-machine deployment to cluster expansion with its strong technical strength and excellent performance. Whether it's a green data center pursuing extreme energy efficiency or a cutting-edge AI application with stringent computing power requirements, this "Intelligent Computing Core" can achieve cost reduction, efficiency increase, and technology upgrade, occupying the development opportunity in the AI wave.
