Imagine you are training an LLM consisting of billions of parameters and your training dataset is significantly larger than the available RAM in your system. Which of the following would be an alternative?
A.
Using the GPU memory to extend the RAM capacity for storing the dataset and move the dataset in and out of the GPU, using the PCI bandwidth possibly.
B.
Using a memory-mapped file that allows the library to access and operate on elements of the dataset without needing to fully load it into memory.
C.
Discarding the excess of data and pruning the dataset to the capacity of the RAM, resulting in reduced latency during inference.
D.
Eliminating sentences that are syntactically different by semantically equivalent, possibly reducing the risk of the model hallucinating as it is trained to get to the point.
When training an LLM with a dataset larger than available RAM, using a memory-mapped file is an effective alternative, as discussed in NVIDIA’s Generative AI and LLMs course. Memory-mapped files allow the system to access portions of the dataset directly from disk without loading the entire dataset into RAM, enabling efficient handling of large datasets. This approach leverages virtual memory to map file contents to memory, reducing memory bottlenecks. Option A is incorrect, as moving large datasets in and out of GPU memory via PCI bandwidth is inefficient and not a standard practice for dataset storage. Option C is wrong, as discarding data reduces model quality and is not a scalable solution. Option D is inaccurate, as eliminating semantically equivalent sentences is a specific preprocessing step that does not address memory constraints. The course states: “Memory-mapped files enable efficient training of LLMs on large datasets by accessing data from disk without loading it fully into RAM, overcoming memory limitations.”
[References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing., ]
Contribute your Thoughts:
Chosen Answer:
This is a voting comment (?). You can switch to a simple comment. It is better to Upvote an existing comment if you don't have anything to add.
Submit