Large-scale Transformer-based models trained for generation tasks (eg, GPT-3) have
recently attracted huge interest, emphasizing the need for system support for serving models …

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural
language processing (NLP). LLMs have revolutionized a multitude of domains, and they …

In recent years, large language models (LLMs) have achieved remarkable success in
natural language processing (NLP). LLMs require an extreme amount of parameters to …

Transformers are state-of-the-art in a wide range of NLP tasks and have also been applied
to many real-world products. Understanding the reliability and certainty of transformer …

Traditional recommender systems are typically passive in that they try to adapt their
recommendations to the user's historical interests. However, it is highly desirable for …

Micro-enterprises and individual developers emerge analysis demands for long sequence
with powerful Large Language Models (LLMs). They try to deploy the LLMs at local, but only …

We introduce a novel tensor contraction processor (TCP) architecture that offers a paradigm
shift from traditional architectures that rely on fixed-size matrix multiplications. TCP aims at …

Large Language Models (LLMs) are becoming ubiquitous across industries, where
applications demand they fulfill diverse user intents. However, developers currently face the …

An inference system applies a machine-learning transformer model to a batch of requests
with variable input length or variable target length or variable internal state length by …

An inference system applies a machine-learning transformer model to a batch of requests
with variable input length or variable target length or variable internal state length by …