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.
0
0
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 ...
0
0
Integrating large language models into existing business processes and systems may encounter technical challenges, such as compatibility issues and system performance bottlenecks.
0
0
Large language model inference requires a considerable amount of computing and storage resources, leading to significant cost pressure for enterprises.
0
0
Ensuring data security and privacy protection during large-scale model inference processes is a significant challenge for enterprises.
0
0
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.
0
0
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.
0
0
Computing power centers require rapid iteration and updates to meet evolving market demands.
0
0
Computing power centers need to ensure that operations and maintenance activities comply with relevant laws, regulations, and industry standards.
0
0
Data security is crucial for computing power centers, necessitating measures to prevent unauthorized access and data breaches.
0
0
