Hello Tong,

Training Large Language models in Machine Learning using Natural Language processing techniques is an advanced approach to resolve realworld problems related to language models and Llama. The significance of these models relies on the volume of data and the languages consumed through various social networks including X, Meta, News Articles, Research article websites, Organizational componenets used from company websites, healthcare, finance and other domain related organization third party data provider websites and applications provide required data for managing these required datasets.

Such an auditious datasets will be stored in various technical datastorage options including Relational databases(RDBMS), Firestore(Firebased DB to store Json, files data for XML and other formats etc...), Google's BigQuery Analytics offers various datastorage options such as Mongo DB, Terraform, PostgreSQL, Microsoft SQL Server, Oracle Autonomous cloud datastorage, SQLite etc...

Firestorage works as an Hierarchial data storage to keep any file in the format of Json with in the Firestore database. It helps in resolving the storage issues related to NAS system in traditional windows operating system. MongoDB offers solutions to the NoSQL databases as there are other databases like Casandra and advanced Hadoop based databases provides the flexibility to transfer data between the traditional systems and HDFS/NFS systems.

Traditional RDBMS databases includes Oracle Autonomous Cloud, MS SQL Server in Azure/AWS/Google/Oracle Cloud, Snowflake provides cloud based functionalities facilitates required relational structure storage capabilities.

If the data is from an Organizational websites to build and train Machine Learning(ML) based model to inference it later in the process of extrapolation would recommend to use CSV, TXT and EXCEL files using the python based modules to store data in data structures like simple vector/Array/Dictonary and Dataframes to keep the data until the model is trained and make sure the files are stored in local drives of the NFS/UNIX/MAC system files for an transfererable storage options.

Box is another storage option introduced to keep any file extension to create an alternate to System NFS structure in cloud environment.

Data related to multidimensional architectures with hierarichal and Relational structures could enable the option of Oracle Enterprise Performance Management Cloud, Oracle Financial Consolidation and Close cloud system, Oracle Account Reconciliation system, Oracle TaxRivision System etc.. to help Organizations with Financial applications need in Cloud environment.

Training large language models (LLMs) calls for high throughput, low latency, parallel access storage devices. Because they can manage a lot of I/O and reads and writes occurring simultaneously across GPU clusters, distributed file systems: like GPFS (IBM Spectrum Scale) or Lustre work better than regular NAS. Though HDFS is scalable, for high-performance deep learning applications its latency and lack of POSIX compatibility make it less than perfect. One fresh concept is combining NVMe-over-Fabrics with a tiered storage architecture. Use quick local NVMe for active data; object storage like Ceph or S3 for cold or archived data. To reduce I/O congestion, smart storage orchestration layers—such as NVIDIA Magnum IO or Alluxio, may also store and prefetch training data. Combining GPU aware scheduling with software-defined storage increases the pipeline's overall efficiency.