英文参考原文：Based on the information available, here’s a comparison of vLLM and Ollama, two popular frameworks for running large language models (LLMs) locally:
vLLM
Focus: High-throughput, low-latency LLM inference and serving, particularly suited for production environments.
Key Features:
PagedAttention: A memory management technique that optimizes GPU memory usage for faster inference speeds, especially with long sequences and large models.
Continuous Batching: Processes incoming requests dynamically to maximize hardware utilization.
High Performance: Consistently delivers superior throughput and lower latency, particularly for concurrent requests.
Scalability: Designed for scalability, including support for tensor parallelism and pipeline parallelism for distributed inference across multiple GPUs or nodes.
OpenAI-compatible API: Simplifies integration with applications.
Hardware Requirements: Optimized for high-end, CUDA-enabled NVIDIA GPUs, although it technically supports CPU inference (less optimized).
Ease of Use: Offers more control and optimization options but has a steeper learning curve, requiring more technical knowledge for setup.
Ollama
Focus: User-friendly, local deployment and management of LLMs, prioritizing simplicity and accessibility.
Key Features:
Ease of Use: Offers a streamlined workflow for downloading, running, and managing models with a simple command-line interface (CLI) and an OpenAI-compatible API.
Broad Hardware Compatibility: Works well on both GPUs and CPUs, making it accessible to users with consumer-grade hardware.
Local Deployment with Privacy: Ensures data privacy and control by keeping data processing within your local environment.
Adaptable: Supports various model types and offers token streaming for faster responses.
Growing Performance: While potentially slower than vLLM on high-end GPUs, recent updates have significantly improved its performance.
Hardware Requirements: Designed to work reasonably well even on consumer-grade hardware.
Ease of Use: Prioritizes simplicity, making it easy to install and run models with just a few commands.
In Summary:
Choose vLLM when: You need maximum performance and scalability in production environments, especially when utilizing high-end GPUs for high-throughput workloads.
Choose Ollama when: You prioritize ease of use, broad hardware compatibility (including CPU-only setups), and local data privacy for development, prototyping, or simpler projects.
Hybrid Approach:
It’s worth considering a hybrid approach where you use Ollama for development and prototyping and then deploy with vLLM in production for optimal performance.
