Autoregressive Generation

The inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.

The transition problem in AI video stems from the autoregressive generation approach of models, where each video clip is independently sampled and generated, lacking cross-clip temporal consistency constraints.

The generation phase is essentially a serial process that cannot be fully parallelized the way input encoding can, resulting in relatively low GPU utilization—this is the technical reason why output costs are inherently higher than input costs.

GPT-4 and Claude are large language models that can understand context, generate structured outputs, and execute multi-step reasoning

Output Tokens are typically 2-4x more expensive than input Tokens because generation requires autoregressive decoding Token by Token, with each generated Token requiring a complete forward pass computation

During the autoregressive decoding phase, GPU compute utilization is often less than 10%, making it a memory-intensive operation.

在自回归生成中，随着文本长度增加，早期设定的约束条件在概率分布中的权重逐渐降低，导致生成内容偏离原始意图

In language modeling, the Transformer is essentially learning the conditional probability distribution P(xt|x1,...,xt-1) of token sequences, and autoregressive generation is the process of sequentially sampling from this probability distribution.

Goal Drift in AI Agents stems from the autoregressive generation mechanism of large language models, where small early deviations are progressively amplified during long sequence generation.

The topic drift phenomenon that occurs in long-text generation by large language models is academically referred to as Semantic Drift, and its root cause lies in the autoregressive generation mechanism.

NTP is the training objective and autoregressive generation is the inference method, both based on the same mechanism of predicting the next token from the preceding context.

Autoregressive generation outputs are affected by sampling strategies (hyperparameters such as temperature and top-p), making it difficult to guarantee completely consistent outputs

A typical LLM uses an autoregressive generation approach, predicting token by token with each step's output feeding into the next step as input, forming a loop

For autoregressive generation, a task that outputs tokens one by one, memory bandwidth is often the key factor determining inference speed.

During the autoregressive generation process, each parallel request requires its own independent KV cache space, and these caches cannot be shared

The arithmetic intensity of the decoding stage in LLM autoregressive generation is extremely low: for a 7B-parameter FP16 model, one forward pass requires loading approximately 14 GB of weights but executes only about 14 GFLOPs of computation, resulting in an arithmetic intensity of approximately 1 FLOP/Byte, making it a bandwidth-bound workload.

In autoregressive generation, producing a response containing 500 tokens requires 500 forward passes, which cannot be parallelized; whereas perception tasks such as image classification require only a single forward pass.

Traditional autoregressive generation can only produce one token at a time and requires a full forward pass, resulting in low GPU utilization, with the actual idle rate of compute units potentially exceeding 80%.

LLMs generate text in an autoregressive manner, generating only one token at a time and appending it to the input sequence, requiring 100 complete forward passes through the model to generate 100 tokens.

Autoregressive generation is the foundational paradigm for virtually all current large language models, generating text sequences token by token; the GPT series, LLaMA, and Claude all adopt this paradigm.