Insufficient computing resources
With the continuous expansion of artificial intelligence model scales, enterprises face the challenge of inadequate computing resources, unable to meet the demand for large-scale model inference.
With the continuous expansion of artificial intelligence model scales, enterprises face the challenge of inadequate computing resources, unable to meet the demand for large-scale model inference.
As the model scale increases, the requirements for hardware also become higher. Existing hardware may be unable to support the inference of extra-large models, necessitating dedicated hardware design ...
Integrating large language models into existing business processes and systems may encounter technical challenges, such as compatibility issues and system performance bottlenecks.
Large language model inference requires a considerable amount of computing and storage resources, leading to significant cost pressure for enterprises.
Ensuring data security and privacy protection during large-scale model inference processes is a significant challenge for enterprises.
In application scenarios with high real-time requirements, such as financial transactions, online gaming, etc., network latency becomes a major factor limiting model inference efficiency.
Computing power centers need to establish and maintain a set of standard operations and maintenance processes to ensure the standardization and normalization of operations and maintenance.
Computing power centers require rapid iteration and updates to meet evolving market demands.
Computing power centers need to ensure that operations and maintenance activities comply with relevant laws, regulations, and industry standards.
Data security is crucial for computing power centers, necessitating measures to prevent unauthorized access and data breaches.