Abstract: A ring buffer storage method includes generating data of a first output according to Q input tokens of a large language model (LLM), and writing the data of the first output into last Q column vectors of an updated first cache tensor buffer matrix. A starting memory address of a first cache tensor buffer is shifted according to the number Q of input tokens of the LLM for updating the first cache tensor buffer. The first cache tensor buffer forms a first cache tensor buffer matrix. The updated first cache tensor buffer forms the updated first cache tensor buffer matrix. The first cache tensor buffer matrix includes a plurality of space segments. Each row of the first cache tensor buffer matrix includes C space segments. C is a cache size. The plurality of space segments have continuous memory addresses.

Abstract: An auto-regressive method for a large language model includes receiving a hidden state associated with at least one token, generating key data, first value data, and query data according to a received hidden state, generating first positionally encoded key data by encoding the key data positionally, generating positionally encoded query data by encoding the query data positionally, performing first element-wise dot product operations according to the first positionally encoded key data, the positionally encoded query data, and second positionally encoded key data to generate an attention score, performing second element-wise dot product operations according to the first value data, the attention score, and second value data to generate an attention output, and adding the attention output and the hidden state to generate an updated hidden output.