### Classical Approaches at the Synchrotron Radiation Facilites  
Synchrotron beamlines typically offer two approaches to provide users with on-site data processing and analysis services to address their computationally intensive needs. The first approach involves uploading data and jobs to a national supercomputer via high-speed scientific network infrastructures. The second approach involves the deployment of on-premises high-performance workstations, or small clusters, dealing with the task involving the data processing jobs.  
For example, the Superfacility project [9] at the Lawrence Berkeley National Laboratory (LBNL) links research facilities with the National Energy Research Scientific Computing Center (NERSC)'s high-performance computing (HPC) resources via ESnet [10], allowing for large-scale data analysis with minimal human intervention. This decreases the length of the analysis cycles from days or weeks down to minutes or hours. Users have the possibility of accessing storage, open software, and tools without the need of managing complex architectures or possessing computational competences [11; 12; 13; 14]. The Advanced Light Source (ALS) has launched several projects at the NERSC, including a data portal, a data-sharing service, and an artificial intelligence (AI)/machine learning (ML) collaboration project, streamlining for the users the data ingestion, sharing and labeling processes [15]. This approach adheres to the concept of resource concentration and intensification. However, it is important to note that the resource allocation and scheduling, as well as the queuing time, are beyond the control of the beamline scientists and users, due to the operational regulations of the supercomputer itself. Taking the example of the ALS using the SPOT framework and NERSC to process tomography data, the actual job execution time for computed tomography (CT) reconstructions was less than 10 min, while the queuing time in the NERSC scheduling system was circa 30 min [15].  
The TOMCAT beamline, at Swiss Light Source, has instead adopted an on-premises computing system approach, installing the GigaFRoST detector system for quick data acquisition [16] and creating an effective tomographic reconstruction pipeline using high-performance computing to manage and analyze the massive data influx [17; 18]. The TomoPy framework [19], developed by the Advanced Photon Source (APS) using Python, represents a highly effective data-intensive strategy. ALS has adopted and further developed the TomoPy framework by implementing a modernized user interface [20], which can considerably increase the CT users' workflow efficiency. Until 2019, the macromolecular crystallography (MX) beamlines at the SSRF utilized an automated system, Aquarium [21], which employed a local dedicated high-performance computing cluster for large-scale parallel computations. This expedites the data reduction, single-wavelength anomalous diffraction (SAD) phasing, and model construction procedures, which took place within a 5 to 10 min time window. Although local dedicated small-scale computing clusters can ensure real-time job execution through resource exclusivity, they come with the limits of higher economic costs and a lower scalability.  
Integrating the two approaches could then result in a more efficient solution that prioritizes the usage of local dedicated infrastructures matching the real-time needs of the users' experiments, and then allocates computational tasks to large computing centers when higher computational demands arise, all within a framework designed to accommodate the needs of the diverse scientific communities. This hybrid approach will provide substantial benefits but requires close collaboration between the local computing infrastructures at the beamlines and the large computing centers in order to ensure a seamless integration and an efficient data transfer.