Transformer

An LLM (large language model) is a deep neural network—almost always a Transformer—trained on large amounts of text (and sometimes multimodal data) to model the probability of the next token given prior context. Its objective at training time is to minimize prediction error over billions of tokens, producing weights that encode grammar, facts (with limitations), reasoning patterns, and task-following behavior after alignment or instruction tuning. At inference time the same model generates completions, answers questions, summarizes documents, or drives agents; production systems expose it through APIs (often OpenAI-compatible) backed by engines such as vLLM or NIM. LLMs power chatbots, code assistants, RAG pipelines, and enterprise copilots.

The KV cache (key-value cache) is the stored result of the attention layers for tokens already processed in a sequence. During autoregressive decode, each new token only needs a forward pass that depends on prior context; recomputing keys and values for all earlier tokens every step would be wasteful. The cache therefore holds, per layer and per sequence, the K and V tensors produced when those tokens were first seen (during prefill for the prompt, then extended one token at a time during decode). The objective is lower time per output token and lower FLOPs; the cost is GPU memory: cache size grows with batch × layers × heads × sequence_length × head_dim, and is often the limit on concurrent sessions or context length before model weights fill VRAM.