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.
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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
- 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
- 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 (/chat/completions)
- 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)
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3. Practical Usage Examples
Here are quick, real-world commands to help you get started with each deployment method:
▶️ Run Mistral Locally Using Ollama
ollama run mistral
Runs a quantized Mistral model on your local machine in seconds — no extra setup required.
⚙️ Serve a GPT-Compatible API with vLLM
python -m vllm.entrypoints.openai.api_server \ --model facebook/opt-1.3b
Launches a high-performance OpenAI-style API endpoint using vLLM with an OPT 1.3B model. Compatible with /chat/completions.
🐳 Deploy Mistral via Docker with TG
docker run -p 8080:80 \ ghcr.io/huggingface/text-generation-inference \ --model-id mistralai/Mistral-7B-Instruct-v0.1
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 🟠 Ollamais fast enough for local use 🔵 OpenAI API provides convenience but is slower due to network/API latency
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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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