Assess your understanding of training efficiency and infrastructure considerations in large language model (LLM) scaling. This quiz covers core concepts such as parallelism strategies, resource optimization, hardware utilization, and efficient data management for LLMs.
Which parallelism strategy involves splitting different parts of a neural network model across multiple devices, such as dividing layers among machines?
Explanation: Model parallelism is the technique of dividing a model’s architecture so each device computes a different part, such as separate layers. Data parallelism, in contrast, replicates the entire model on multiple devices and splits batches among them. Task parallelism involves letting different devices handle unrelated tasks rather than cooperating on a single process. Bit parallelism, which generally refers to processing bits simultaneously within a device, is not a relevant strategy for distributing a neural network model.
If a team wants to accelerate LLM training without changing the model’s accuracy, which method is most likely to help without direct impact on results?
Explanation: Using mixed-precision arithmetic allows faster computations with less memory usage while often maintaining accuracy. Skipping validation steps doesn’t affect training speed, just result monitoring. Training with corrupted data harms performance and accuracy. Increasing batch size beyond capacity results in memory errors and won’t improve efficiency.
Which data storage approach typically improves I/O performance when handling large text datasets for training LLMs?
Explanation: Sharded binary formats allow for quicker data reads and parallel access compared to a single, massive text file, which can be slow and unwieldy. Cloud email storage is not designed for large-scale machine learning and lacks necessary speed. Lossy compression risks damaging training data integrity by introducing irrecoverable errors.
When running an LLM on limited GPU memory, which practice helps reduce memory usage during training?
Explanation: Gradient checkpointing stores fewer intermediate results, freeing up memory at the cost of extra computation. Using larger inputs increases memory demands. Disabling batch normalization may affect model performance but doesn’t significantly reduce memory needs. Using double precision increases memory usage rather than decreasing it.
During LLM training, what is an effective strategy to keep computational resources busy and prevent idle time due to slow data access?
Explanation: Prefetching allows the next batch of data to be loaded while the current one is processed, reducing downtime. Lowering the learning rate adjusts model training dynamics but doesn’t influence data I/O. Outdated drivers may reduce efficiency, and disabling data shuffling can introduce bias but doesn’t address data access speed.
A team wants to scale LLM training over many compute nodes; which aspect is most important to limit wasted computation across those nodes?
Explanation: Efficient node communication ensures synchronized updates and limits idle time, key for distributed training. Increasing sequence length increases computational demand. Reducing data size can limit the model’s learning, and lowering model depth changes its capacity rather than addressing scaling bottlenecks.
Which technique is commonly used to manage extremely large embedding tables that can't fit on a single device during LLM training?
Explanation: Parameter sharding distributes large parameter sets, like embedding tables, across multiple devices to manage memory limits. Batch normalization normalizes activations and doesn’t solve embedding size. Label smoothing changes target distributions and is unrelated. Gradient reversal is used in specific learning scenarios but not for memory management.
Which practice can improve the energy efficiency of LLM infrastructure during training?
Explanation: Matching hardware to the training task prevents waste and improves energy efficiency. Maximizing idle time and keeping all hardware running increases energy waste. Delaying upgrades can lead to continued use of outdated, inefficient hardware and does not promote energy savings.
If some data shards are much larger than others in distributed LLM training, what problem can occur?
Explanation: When shards are imbalanced, some nodes finish early and wait while others catch up, causing idle resources. Learning rate, vocabulary size, and data shuffling are independent of workload distribution and are not directly affected by data shard size differences.
In distributed LLM training, why is it important to use efficient checkpointing mechanisms?
Explanation: Efficient checkpointing allows models to be saved and restored rapidly, minimizing lost progress after failures. It does not directly improve log clarity, increase epoch size, or purposefully slow training. The primary goal is to ensure continuity and resilience during training.