Transformer-based large language models (LLMs) utilize autoregressive decomposition, providing an effective method for modeling the probabilities of token sequences in natural languages.
Current LLM serving systems face substantial memory challenges, necessitating innovative techniques such as PagedAttention and KV Cache Manager to optimize memory management.
The proposed solutions, including decoding enhancements and distributed execution, aim to improve efficiency and reduce latency in serving transformer-based large language models.
Experiments showcase the effectiveness of the introduced methods in practical LLM service contexts, indicating their potential applications beyond mere sampling to broader decoding scenarios.
Collection
[
|
...
]