Top AI Model Containers and How to Use Them Effectively (2025)

• Leo Zhi
• March 16, 2025
• 9:00 am

Recently, DeepSeek has been gaining immense popularity. However, many users may have encountered the “server busy” issue while using it on the web or app platforms. This problem has sparked growing interest in local deployment of large language models. However, for ordinary users, large language models come with high technical barriers, including massive parameter sizes, significant GPU and memory requirements for inference, and complex installation and configuration processes. This is where “model container” tools come in. They function like dedicated suitcases for AI models, packaging complex installation steps and hardware optimizations to make them easier to use.

Today, we’ll introduce five popular tools that even beginners can use, ranging from no-code options to developer-friendly solutions, helping you find the best “suitcase” for your needs.

01

Best for Beginners: LM Studio

If you just want to try out large models on your computer without dealing with code, LM Studio is designed for you. It works like an app store—simply open it and download various models, such as Mistral and Llama, including Chinese-language options. Once installed, you can load models with a few clicks and chat with AI directly in the software. For example, if you download the “Mistral-7B” model, it will automatically detect your GPU and optimize performance (supporting both NVIDIA and Apple computers), eliminating the need for manual environment configuration.

However, LM Studio is only for local use and cannot be turned into a website or app for others to access. It’s ideal for private AI research or handling confidential data (such as analyzing personal diaries). If your computer has less than 16GB of RAM, it’s recommended to use models smaller than 7B to avoid lag.

LM Studio: https://lmstudio.ai/

02

Ideal for Developers: Ollama

Many developers prefer working in the terminal (the black command-line interface), and Ollama is built specifically for them. After installation, you can start the latest Llama 3 model with a simple command like ollama run llama3.

Ollama’s biggest advantage is flexibility—you can adjust temperature settings (controlling AI’s creativity), modify system prompts, or even convert models from Hugging Face into a compatible format.

For example, if you want AI to write in the style of Lu Xun, you can create a configuration file (called a Modelfile) and specify: “You are an assistant mimicking Lu Xun’s writing style, creating works in vernacular Chinese.” This is particularly useful for automation tasks, such as generating product descriptions in bulk or auto-replying to emails. However, beginners might need some time to get familiar with command-line operations, though the official documentation is quite detailed.

Ollama: https://ollama.com/download

03

High-Performance Solution: vLLM

If your application needs to serve many users simultaneously (such as running a public AI chatbot website), regular tools may struggle to keep up. This is where vLLM comes in, offering over 20 times faster processing speeds than conventional methods. Originally developed by the University of California, Berkeley, vLLM is optimized for high-concurrency scenarios. It’s also developer-friendly—after installation, you can launch a service with a simple Python script. Moreover, its API is fully compatible with OpenAI, meaning existing ChatGPT-based code can be migrated with minimal changes.

For instance, if you’re building an AI writing assistant website using vLLM to deploy Llama 3, it can efficiently handle 100 simultaneous poetry requests. However, to fully utilize vLLM, a high-performance GPU (such as an NVIDIA RTX 3090) is recommended, as standard laptops may struggle with large models.

vLLM: https://docs.vllm.ai/en/latest/

04

Enterprise-Grade Solution: Hugging Face TGI

Hugging Face, often called the GitHub of AI, offers Text Generation Inference (TGI), a tool specifically designed for stable, long-term deployments. It supports Docker container-based deployment, ensuring security and allowing users to monitor metrics such as memory consumption and response speed. Many companies use it to deploy models on cloud servers (such as Alibaba Cloud or AWS), and it supports streaming output—similar to how ChatGPT displays text word by word.

However, TGI has high hardware requirements, typically needing GPUs with at least 24GB of VRAM. Fortunately, it supports quantization, reducing model size to a quarter of the original. For example, a 70B model can be compressed to 4-bit format and run efficiently on two A10 GPUs, making it a great choice for small teams with limited budgets.

Hugging Face TGI: https://huggingface.co/text-generation-inference

05

No-Code, Modular AI Development: Flowise

While the tools above focus on individual models, Flowise is designed for building more complex AI applications (such as integrating AI with databases for question-answering). It offers a visual, drag-and-drop interface to connect different modules, such as document loaders, text splitters, and AI models, enabling users to create intelligent applications without writing code. Once completed, the project can be exported as a functional website.

For example, if you want to build an AI assistant that reads financial reports, you can drag a PDF upload module on the left, a text analysis module in the middle, and a Llama model on the right. The process is similar to designing a flowchart, making it ideal for product managers or business professionals to quickly prototype AI applications. However, the final effectiveness depends on the model used, and fine-tuning prompt engineering may be necessary.

Flowise: https://flowiseai.com/

06

How to Choose the Right Tool?

• Beginners: Start with LM Studio—experience AI with just a few clicks.
• Tech Enthusiasts: Try Ollama to learn command-line operations and model parameters.
• Developers with Real Projects: Choose between vLLM (for speed) and Hugging Face TGI (for stability).
• Non-technical Innovators: Use Flowise to build AI applications like assembling Lego blocks.

Final Tips:

• Bigger models aren’t always better—7B models run smoothly on standard computers.
• Pay attention to licensing—commercial use requires checking model licenses (e.g., Llama models require permission from Meta).
• Test locally before deploying to a server to avoid unnecessary costs.

Related:

1. AMD Unveils LM Studio: Your Local Chat App Solution!
2. SemiKong: The First Open-Source Semiconductor AI Model
3. AI Pricing Explained: How Token Usage Affects Cost
4. AI Data Secured by Apple with Confidential Computing

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