The vLLM project is an open-source venture designed to enhance the efficiency and scalability of serving Large Language Models (LLMs). Developed by researchers at UC Berkeley, vLLM aims to improve the performance of LLM inference by optimizing memory management and execution. It offers a system that reduces latency and increases throughput for LLMs, making it a valuable tool for deploying these models more effectively in various applications. It supports multiple LLM model types, multiple hardware architectures, and multiple optimization techniques. It is described in this paper, on Efficient LLM serving with PagedAttention.

vLLM achieves its improvements through

• dynamic batching,
• efficient memory usage, and
• parallel execution strategies.

These features allow it to handle multiple requests simultaneously without sacrificing speed or accuracy.

By making LLMs more accessible and efficient, vLLM helps lower the barriers to using advanced AI models, facilitating broader adoption and innovation in the field of natural language processing. For more detailed information or to contribute to the project, you can explore its repository on platforms like GitHub.

vLLM, NVIDIA Triton Inference Server, and NVIDIA NeMo (formerly known as NVIDIA NIM) are all designed to improve the deployment and performance of machine learning models, but they have different focuses and functionalities. Here’s a comparison of each:

vLLM

• Purpose: Optimizes the serving of Large Language Models (LLMs) with a focus on improving inference efficiency, particularly regarding memory management and execution.
• Features: Offers dynamic batching, efficient memory usage, and parallel execution strategies specifically for LLMs, enhancing latency and throughput.
• Use Cases: Best suited for applications requiring fast, efficient LLM inference, such as AI-driven conversational agents.
• How it reduces memory waste and improves utilization with PagedAttention – https://blog.runpod.io/introduction-to-vllm-and-how-to-run-vllm-on-runpod-serverless/

NVIDIA Triton Inference Server

• Purpose: A scalable and flexible platform for serving different types of machine learning models across a variety of frameworks and hardware architectures.
• Features: Supports multiple model frameworks (e.g., TensorFlow, PyTorch, ONNX), dynamic batching, model versioning, and provides both HTTP/REST and gRPC endpoints for inference requests. It is designed to maximize GPU utilization and streamline inference workflows.
• Use Cases: Ideal for deploying diverse AI models in production environments, allowing for efficient inference at scale across CPUs and GPUs.

NVIDIA NeMo

• Purpose: A toolkit for building, training, and fine-tuning state-of-the-art conversational AI models, including those for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS).
• Features: Provides pre-trained models, model architectures, and training scripts that can be customized and extended for specific tasks. NeMo is designed to facilitate the development of AI models with high accuracy and efficiency.
• Use Cases: Suitable for developers and researchers focused on building and customizing conversational AI applications, offering extensive support for research and development in speech and language domains.

Comparison summary

• Optimization Focus: vLLM is specialized for LLM inference optimization, NVIDIA Triton is a general-purpose inference server supporting various models and frameworks, and NVIDIA NeMo is focused on developing and customizing conversational AI models.
• Hardware and Framework Support: Triton supports a wide range of frameworks and hardware, optimizing inference across diverse environments. NeMo, while capable of leveraging NVIDIA’s hardware optimizations, is more focused on the model training and customization aspect, particularly for conversational AI.
• Target Audience: vLLM targets developers needing efficient LLM deployment; Triton appeals to teams deploying a variety of models in scalable production settings; NeMo is aimed at researchers and developers building state-of-the-art conversational systems.

Details of vLLM PagedAttention.

What Are Keys and Values in PagedAttention?

In the context of transformer-based Large Language Models (LLMs), keys (K) and values (V) are components of the attention mechanism used during inference.

• Keys (K): Represent encoded representations of previous tokens, used to determine how much attention each token should pay to previous tokens.
• Values (V): Contain the actual information used to generate the next token, weighted based on attention scores.

PagedAttention manages these key-value (KV) caches efficiently to store past token embeddings so the model doesn’t have to recompute them in every step, drastically speeding up inference.

Concrete Example: Key-Value Pairs in Action

Let’s take a simple example where an LLM is generating text based on a prompt.

Example Prompt:

User: "The capital of France is"

Tokenized Version (Using Byte-Pair Encoding or SentencePiece):

["The", "capital", "of", "France", "is"]

Each token gets embedded into a high-dimensional space (e.g., 4096 dimensions for LLaMA-2-70B). Let’s assume we use 4096-dimension embeddings for simplicity.

Step-by-Step Key-Value Storage

1. The model encodes each token and stores:
   • Key (K): A vector that helps determine how relevant this token is in future attention computations.
   • Value (V): The actual contextual representation of the token.

2. When generating the next token (“Paris”), the model:
   • Computes attention scores between “Paris” and all previous tokens using dot product of queries (Q) and keys (K).
   • Uses the weighted sum of values (V) to form the new representation.
3. Instead of recomputing attention from scratch, PagedAttention retrieves precomputed (K, V) values from memory pages for fast lookup.

How PagedAttention Optimizes Key-Value Caching

• Without PagedAttention: Each request would store KV pairs in one long, contiguous memory buffer. If a request finishes early, the allocated space is wasted.
• With PagedAttention: KV pairs are stored in small pages (e.g., chunks of 16 tokens), allowing efficient reuse and minimizing fragmentation.