A machine learning team is training a large transformer model on a text corpus. They need to reduce training time while maintaining model accuracy. Which hardware configuration would be MOST effective for this task?
Trap 1: Use a high-core-count CPU with large RAM
CPUs are inefficient for parallel matrix operations common in deep learning; training would be very slow.
Trap 2: Use a single GPU with model parallelism
Model parallelism might be needed for extremely large models, but for most transformers, data parallelism on multiple GPUs is more straightforward and effective.
Trap 3: Use a single TPU with model parallelism
TPU is specialized but less flexible; single TPU may not provide enough compute for large models, and model parallelism adds complexity.
- A
Use a high-core-count CPU with large RAM
Why wrong: CPUs are inefficient for parallel matrix operations common in deep learning; training would be very slow.
- B
Use a cluster of GPUs with data parallelism
GPUs accelerate parallel tensor operations, and data parallelism distributes batches across multiple GPUs, significantly reducing training time.
- C
Use a single GPU with model parallelism
Why wrong: Model parallelism might be needed for extremely large models, but for most transformers, data parallelism on multiple GPUs is more straightforward and effective.
- D
Use a single TPU with model parallelism
Why wrong: TPU is specialized but less flexible; single TPU may not provide enough compute for large models, and model parallelism adds complexity.