This all depends on your use case(s), but here are some options:

• Profile your code to determine where it performs the slowest. Troubleshoot these areas of your code first.
• Speculative decoding (Qwen2-0.5B helping Qwen2-7B, for example)
• Model preloading
• Preloading and/or caching data
• Caching query responses
• Use smaller models
  for embedding/retrieval
• experiment with inference optimizations like torch.compile() and Unsloth/Liger/Marlin
• Use fp8, bfloat16, or float16 torch dtype instead of float32 on GPU.
• Consider a smaller vector DB of summarized data for the first retrieval instead of searching an entire fulltext DB up front.
• Use async code where appropriate

Please note that RAG may not be the best choice for realtime use cases. The best thing to remember is to keep the data as close to the user as possible if you want to get it to the user faster.