Hello Stack Overflow community,
I’m currently exploring various technologies for reducing inference costs in large language models (LLMs) and am particularly interested in the following:
- (VLLM)
- DeepSpeed Inference
- FlexGen
- FlashAttention
- Hugging Face’s Text Generation Inference
- FrugalGPT
- FasterTransformer
- InFLLM
These technologies are well-documented in research papers, but I am looking for real-world applications or case studies where they have been implemented successfully to reduce inference costs in large language models (optimization).
I have researched these technologies and reviewed their academic papers. I expected to find detailed case studies or real-world applications that demonstrate their effectiveness in practical scenarios. However, I found limited information about how these technologies are implemented and their real-world impact.
If you have experience with any of these technologies or know of real-world use cases, could you please share some insights or examples? Specifically, I would like to understand:
How these technologies have been applied in practice.
The impact they had on inference costs.
Any challenges or limitations faced during implementation.
Thank you in advance for your help!