Model deployment: How to choose between Ollama and GPUStack?
Faced with complex and diverse model deployment requirements, how do you choose the right tool? This article compares and analyzes Ollama and GPUStack in depth to provide you with a reference for decision-making.
Core content:
1. Features and applicable scenarios of Ollama lightweight local deployment tool
2. Advantages and limitations of GPUStack enterprise-level GPU/NPU resource management platform
3. How to choose Ollama or GPUStack for model deployment according to specific needs
Ollama
position :
A lightweight local model deployment tool that focuses on quick startup and ease of use, suitable for individual developers or small-scale projects.
Advantages :
Easy installation, supports macOS, Linux, and Windows - via WSL2.
Provides a Docker-like experience, supports multi-model parallel running and OpenAI-compatible API.
The community is active and the model library is rich, such as Gemma, Mistral, etc.
Suitable for quick testing, prototyping, or users who need flexible command line operations.
Limitations :
Feature updates may lag behind the underlying framework, such as llama.cpp.
Advanced features such as distributed inference are limited and rely on local hardware performance.
GPUStack
position :
An enterprise-level GPU/NPU resource management platform that supports heterogeneous hardware and distributed reasoning, suitable for large-scale production environments.
Advantages :
Supports GPU/NPU integration from multiple brands including Nvidia, Apple Metal, and Huawei Ascend.
Compatible with inference backends such as vLLM and llama-box, and supports multi-model repositories including HuggingFace and Ollama Library.
Provides enterprise-level functions such as distributed reasoning, real-time monitoring, and scheduling strategies.
It can be seamlessly connected to the RAG system through tools such as Dify, which is suitable for building complex AI services.
Limitations :
The deployment and configuration are relatively complex and require familiarity with Docker and cluster management.
There is limited native support for Ollama models.
