Batch size significantly impacts embedding generation throughput, primarily by balancing hardware utilization and computational efficiency. Throughput here refers to how many embeddings a system can produce per second. When you increase batch size, you process more data points in a single forward pass through the model, which can better leverage parallel processing capabilities of GPUs or TPUs. However, this comes with trade-offs related to memory usage and diminishing returns. For example, a batch size of 32 might process 100 embeddings per second, while a batch size of 128 could reach 300 embeddings per second on the same hardware, assuming sufficient memory. But pushing batch sizes too high might cause memory errors or even slow down processing due to resource contention.
The relationship between batch size and throughput isn’t linear. Initially, larger batches improve throughput because GPUs process matrix operations more efficiently when handling larger chunks of data. For instance, a GPU with 16GB of memory might handle a batch size of 64 embeddings without breaking a sweat, but a batch size of 256 could exhaust memory and force the system to spill data to slower RAM, drastically reducing throughput. Additionally, preprocessing steps like tokenization or padding might take longer for larger batches, offsetting gains from parallel computation. A practical example: A transformer model like BERT might see throughput plateau at a batch size of 128 on a mid-tier GPU, as memory bandwidth and kernel optimization limits kick in. Beyond this point, increasing batch size might not improve throughput and could even degrade latency (the time to process one batch), which matters for real-time applications.
Choosing the optimal batch size requires testing and depends on hardware constraints and model architecture. For instance, on a consumer-grade GPU with 8GB VRAM, a batch size of 32 might maximize throughput without triggering out-of-memory errors. In contrast, a data center GPU with 40GB VRAM could handle batches of 512 or higher. Developers should profile their systems by incrementally increasing batch size while monitoring metrics like GPU memory usage, processing time per batch, and total embeddings per second. Tools like PyTorch’s torch.utils.benchmark or TensorFlow’s tf.data utilities can help automate this analysis. For example, a test might reveal that doubling the batch size from 64 to 128 only improves throughput by 20% but increases latency by 50%, making it unsuitable for applications requiring quick turnaround for small requests. Balancing these factors ensures efficient resource use without overloading the system.