LLM Deployment 101: Which Method Should You Use and When?

Large Language Models (LLMs) like DeepSeek, Mistral, and LLaMA have gone from research labs to real-world applications — powering chatbots, search engines, personal assistants, and enterprise AI tools.

But getting these models into production isn’t a plug-and-play operation. It involves critical architectural decisions — especially around LLM deployment strategies.

In this article, we’ll explore the most common LLM deployment methods, compare them side by side, and help you decide which to use and when — with visuals, real-world use cases, and performance data.

1. First Question: Cloud or Self-Hosted?

Before choosing a deployment method, answer this:

Do you want to use a hosted API (cloud-based) or run the model on your own infrastructure (self-hosted)?

Cloud-Based (OpenAI, Gemini, Anthropic, etc.)

Pros:

• No setup required

• State-of-the-art models (GPT-4, Claude 3, Gemini Pro)

• Easy scaling, managed services

Cons:

• Pay per usage (token-based pricing)

• Your data is sent to third-party servers

• Limited customization

Best for: MVPs, rapid prototyping, startups with limited infra, or when you need best-in-class models without worrying about hosting.

Self-Hosted (vLLM, Ollama, TGI)

You run the model on your own GPU server or local machine.

Pros:

• Full control over data and models

• Potentially cheaper at scale

• Offline and private use

Cons:

• Requires strong hardware (especially for large models)

• Setup, maintenance, and updates are your responsibility

2. Key Deployment Options

🧠 vLLM (Virtual Large Language Model Server)

• HuggingFace-compatible

• Built for performance (uses PagedAttention)

• GPT-compatible APIs ()

• Handles hundreds of concurrent requests

💻 Ollama

• CLI tool for running quantized models locally

• Uses GGUF format (efficient for CPU/GPU)

• Extremely lightweight & fast to set up

🌐 TGI (Text Generation Inference)

• Built by HuggingFace

• Runs models via REST API (Docker-based)

• Supports quantized models and GPU acceleration

3. Practical Usage Examples

Here are quick, real-world commands to help you get started with each deployment method:

▶️ Run Mistral Locally Using Ollama

Runs a quantized Mistral model on your local machine in seconds — no extra setup required.

⚙️ Serve a GPT-Compatible API with vLLM

Launches a high-performance OpenAI-style API endpoint using vLLM with an OPT 1.3B model. Compatible with .

🐳 Deploy Mistral via Docker with TG

Creates a full REST API endpoint with HuggingFace’s TGI, ready to serve the Mistral-7B-Instruct model.

4. Performance Comparison: Tokens per Second

Here’s an example of average token generation speed for different platforms:

🟢 vLLM shines in terms of throughput 🟠 Ollama is fast enough for local use 🔵 OpenAI API provides convenience but is slower due to network/API latency

5. Memory Usage

Local memory (RAM) required to run these models efficiently:

• OpenAI: 0 GB (runs in the cloud)

• vLLM: 18 GB (suitable for 7B+ models)

• Ollama: Lightweight (8 GB for 7B GGUF)

• TGI: Moderate to high, depending on quantization

6. Max Concurrent Requests

Critical for production systems with heavy traffic:

vLLM offers industry-grade scalability, while Ollama is ideal for personal apps or low-traffic internal tools.

7. Final Thoughts: Flexibility Is the Key

There’s no one-size-fits-all when it comes to deploying LLMs.

👉 If you’re building a simple app, an API might suffice. 👉 If you’re scaling traffic, vLLM could save you thousands. 👉 If you want full privacy or need an offline tool, Ollama is a fantastic choice.

Know your use case. Control your costs. Optimize for performance.

Your Turn 🚀

Which deployment method have you used? What worked, what didn’t?

Drop your thoughts or questions in the comments — We’d love to hear your experience!

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