How to Increase Throughput of a LLM Query on a GPU.

So, I asked on stackoverflow how to increase the throughput of a LLM query on a GPU, and people there don’t seem to like my question, so I asked ChatGPT (GPT 4.0) instead. I thought Stackoverflow was much better before to be honest. :(

Here’s the answer from ChatGPT (GPT 4.0):

Increasing the throughput of queries to a large language model (LLM) in the same GPU setting can be achieved in several ways. However, note that these methods might come with trade-offs in terms of accuracy, response time, or complexity. Here are a few general strategies:

1. Batching: This is the most common method for improving throughput. Batching involves running multiple queries simultaneously, which can significantly increase GPU utilization and overall throughput. However, the maximum batch size is often limited by the available GPU memory. Also, all queries in a batch need to finish before any result can be returned, which could increase the latency of individual queries.
2. Model Distillation or Pruning: If your use case can tolerate a slight decrease in model performance, you can consider using a distilled or pruned version of the model. These smaller models require fewer resources, which can increase throughput. Distillation involves training a smaller model to mimic the behavior of the larger model, while pruning involves removing less important parts of the model.
3. Optimize Inference: Using libraries like TensorRT, ONNX Runtime, or TVM can help optimize the model’s inference speed. These libraries can perform operations such as layer fusion, precision reduction, and kernel auto-tuning which can boost the throughput.
4. Parallelization: If your infrastructure allows it, you could run multiple instances of the model on the same GPU, in different CUDA streams. However, managing parallelism at this level can be complex and may not always lead to better throughput, depending on the specifics of your hardware and software stack.
5. Efficient Preprocessing and Postprocessing: The time taken to prepare data (tokenization, etc.) for the model and to interpret its output can also impact throughput. Optimizing these steps can lead to significant improvements.
6. Use Asynchronous Inference: If your application design allows it, using asynchronous inference can help utilize the GPU more effectively. This means that while the GPU is working on inference, the CPU can work on other tasks like data preprocessing for the next batch.
7. Model Quantization: This involves converting the model weights from floating-point representation to lower bit-width integers. This can speed up model inference and reduce memory usage, at the cost of a slight decrease in model accuracy.

Remember to profile and benchmark your application to identify bottlenecks and guide optimization efforts. Also, consider the specific requirements of your application, as different strategies might be more suitable depending on whether you prioritize latency, throughput, or model accuracy.