Introduction to LLM Inference
When you enter a prompt into an AI model, you trigger a complex process known as LLM inference. The model converts your text into numbers, processes them through neural network layers, and returns a response one token at a time. Understanding this mechanism is crucial for anyone looking to optimize AI application performance or costs.
Large Language Models (LLMs) like GPT-4 or Llama are built on the Transformer architecture. Unlike earlier systems that processed text sequentially, Transformers analyze entire sequences in parallel, capturing complex relationships between words thanks to the self-attention mechanism.
Context: Architecture and Tokenization
The fundamental building block of these models is the Transformer layer, consisting of two main components: a self-attention mechanism and a feed-forward neural network. Models stack dozens of these layers to learn deep linguistic patterns. Model size is defined by the number of parameters (weights learned during training) that transform input data.
The Tokenization Process
Before any computation, text must become numbers. Tokenization breaks text into units called tokens. The most common approach, Byte Pair Encoding (BPE), iteratively merges the most frequent character pairs. This allows common words to be represented as single tokens (efficiency) while rare words are broken into sub-units (flexibility).
Technical Note: Tokenization directly impacts costs and latency. Languages other than English may require more tokens to express the same concept, increasing computational load.
Embeddings and Attention Mechanism
Once token IDs are obtained, they are transformed into continuous vectors via an embedding layer. These vectors capture semantic meaning: words with similar meanings will have vectors pointing in similar directions in multidimensional space. Since Transformers do not inherently understand order, positional encodings are added to indicate each token's position in the sequence.
Self-Attention: Query, Key, and Value
The core of LLM inference is the attention calculation. For each token, the model generates three matrices: Query (Q), Key (K), and Value (V). The mechanism calculates how much each token should "attend" to others, allowing the model to understand the global context of the sentence.
Inference Phases: Prefill and Decode
Inference is divided into two distinct phases with opposite computational characteristics:
- Prefill Phase (Compute-Bound): Occurs when you submit the prompt. The model processes all input tokens in parallel. It is GPU-intensive, computing attention matrices for all token pairs simultaneously. This phase determines the Time to First Token (TTFT).
- Decode Phase (Memory-Bound): Begins after the first token is generated. The model produces output autoregressively, one at a time. Here, the GPU spends more time loading data from memory than calculating, as each new token requires comparison with all previous ones.
Optimization: The KV Cache
Without optimization, generating each new token would require recomputing attention for the entire previous sequence. The KV Cache solves this by saving the Key and Value matrices of processed tokens. During generation, the model only computes the Query for the new token and retrieves K and V from the cache.
Although the KV Cache drastically speeds up inference (up to 5x), it carries a high memory (VRAM) cost. With long contexts, the cache can occupy gigabytes of space, becoming a critical bottleneck.
Quantization and Performance Metrics
To manage memory requirements, inference often operates at reduced precision. While training uses formats like FP32 or BF16, inference can use FP16, INT8, or even INT4 with minimal quality loss. Quantization reduces memory usage and increases throughput.
Key Metrics to Monitor
- Time to First Token (TTFT): The latency perceived by the user before seeing the start of the response.
- Inter-Token Latency (ITL): The time between generating one token and the next; determines streaming smoothness.
- Throughput: The total number of tokens generated per second, indicating system capacity.
Conclusion
LLM inference is a balance between compute power and memory bandwidth. From initial tokenization to intelligent KV Cache management, every step is optimized to transform number matrices into coherent text. Using advanced serving frameworks like vLLM or TensorRT-LLM and quantization techniques is essential to deploy these technologies efficiently in production.
FAQ
What is LLM inference?
It is the process where a language model processes an input prompt (prefill) and generates a text response (decode) based on learned patterns.
Why is KV Cache important for inference?
The KV Cache stores calculations for previous tokens, avoiding the need to reprocess them for every new token generated, drastically improving speed.
What is the difference between prefill and decode phases?
Prefill processes the entire prompt in parallel and is compute-bound; decode generates one token at a time and is memory-bound.
How does tokenization affect costs?
More tokens mean more calculations and memory. Texts in languages other than English may generate more tokens, increasing costs and latency.
What does Time to First Token (TTFT) indicate?
It measures the time between sending the request and seeing the first token of the response; it is crucial for perceived responsiveness.