To manage GPU memory and avoid Out of Memory (OOM) errors during the embedding process, consider implementing the following strategies:

1. Limit Batch Size: Ensure your data is processed in smaller batches that do not exceed the maximum batch size limit, which is set to 259 in the NVIDIAEmbedding class. This helps in preventing excessive GPU memory usage.

2. Truncate Input Text: Use the truncation feature in the NVIDIAEmbedding class to keep the input text within the model's maximum token length, reducing memory usage.

3. Optimize Memory Usage: After each embedding operation, use torch.cuda.empty_cache() and gc.collect() to clear unused memory from the GPU and trigger garbage collection in Python, respectively. This can help in efficiently managing GPU memory.

4. Adjust Configurable Parameters: Experiment with adjusting parameters such as DEFAULT_EMBED_BATCH_SIZE, DEFAULT_CONTEXT_WINDOW, and others to find a balance that works best for your specific use case and helps in managing GPU memory usage.

5. Utilize Error Handling Mechanisms: The codebase includes error handling for embedding processes, which can be extended or adapted to manage GPU OOM errors by logging or taking recovery actions through the callback system.

By carefully managing batch sizes, input text length, and memory usage, and by adjusting configurable parameters, you can mitigate the risk of GPU OOM errors during the embedding process.

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