Local LLM Deployment: How to Run Large Language Models on Your Own Hardware Privately and Cheaply

Why Local LLM Deployment Is Becoming Essential

Running LLMs through cloud APIs costs money per token. Every query costs something. With thousands of daily queries, costs accumulate quickly. OpenAI's API costs roughly $0.03 per 1,000 tokens for GPT-4. At just 100,000 tokens daily, that's $90 monthly. Running the same model locally costs roughly $5 monthly electricity on a consumer GPU.

Beyond cost, local deployment means data stays on your hardware. Nothing gets sent to external APIs. For sensitive business data, medical records, legal documents, or proprietary information, this privacy benefit alone often justifies local deployment.

Hardware Requirements for Local Deployment

You don't need enterprise infrastructure. Consumer hardware works fine for many use cases.

Minimum Requirements

• GPU Memory: 8GB for 7B models, 16GB for 13B models, 24GB for 30B models. Older consumer GPUs work: RTX 3080, RTX 3090, RTX 4080.
• Storage: Models need storage for weights. 7B models take 4 to 8GB, 13B models 8 to 16GB, 30B models 16 to 32GB.
• CPU: Any modern CPU (Intel i7 or equivalent) works. Doesn't need to be high end.
• RAM: 16GB minimum, 32GB recommended. Larger RAM allows bigger batch sizes and faster processing.

Optimal Setup for Production

RTX 4090 (24GB VRAM) runs 30B models comfortably or multiple concurrent requests on smaller models. Dual GPUs using tensor parallelism handle 70B models. A-100 GPUs (80GB) run 70B models with room for multiple simultaneous users.

For most small to medium organizations, a single RTX 4090 represents excellent value: $1,600 upfront cost, roughly $200 yearly electricity, infinite token processing.

Cost Comparison: Local vs Cloud

Cloud APIs cost $3 to $10 per 1,000 tokens depending on model size. Processing 1 million tokens monthly costs $3,000 to $10,000. A $1,600 GPU pays for itself within 3 to 6 months through usage.

Setting Up Local Deployment: Step by Step

Method 1: Using Ollama (Simplest)

Ollama provides the easiest entry point for local LLM deployment.

Step 1: Download and Install Ollama Visit ollama.ai and download the installer for your operating system (Windows, macOS, Linux). Run the installer and complete setup.

Step 2: Open Terminal or Command Prompt Type "ollama" to verify installation worked and see the command interface.

Step 3: Browse Available Models Visit ollama.ai/models and browse available models. Llama 3.1 (8B, 13B, 70B), Mistral Small 3, DeepSeek and others are available.

Step 4: Run a Model Copy the command for your chosen model (e.g., "ollama run llama3.1") and paste into terminal. Ollama downloads and starts the model. First download takes 5 to 30 minutes depending on model size and internet speed.

Step 5: Interact With the Model Type your prompts directly. The model responds conversationally. Press Ctrl+C to exit or type "/quit".

Step 6: Access Via API Ollama exposes an API on localhost:11434. Send requests programmatically: curl http://localhost:11434/api/generate -d '{"model": "llama3.1", "prompt": "Why is the sky blue?"}' returns JSON responses with model output.

Method 2: Using LM Studio (GUI Alternative)

Step 1: Download LM Studio Visit lmstudio.ai and download for your platform.

Step 2: Launch the Application Open LM Studio. The GUI shows available models and your system specs.

Step 3: Download a Model Browse models, select one (Llama 3.1, Mistral, etc.), click download. Models cache to your storage after first download.

Step 4: Configure Settings Set context window, temperature, top-p sampling, and other parameters before generation.

Step 5: Chat or API Mode Chat mode lets you type queries and see responses interactively. API mode exposes a local API you connect to from external applications.

Method 3: Using vLLM for Production

For production deployments needing high throughput, vLLM is the best choice.

Install vLLM: pip install vllm. Start server: vllm serve meta-llama/Llama-2-7b-hf. Connect applications to localhost:8000. vLLM handles batching, paging attention, and parallel processing automatically.

Integrating Local LLMs Into Applications

Once your local model runs, integrate it into applications. Most frameworks provide client libraries.

Python Integration

Using LangChain with local Ollama: from langchain.llms import Ollama. llm = Ollama(model="llama3.1"). response = llm("Your prompt"). This simple code uses your local model through Python.

JavaScript Integration

JavaScript can query the local API: fetch('http://localhost:11434/api/generate', {method: 'POST', body: JSON.stringify({model: 'llama3.1', prompt: 'Your prompt'})}).then(r => r.json()).then(d => console.log(d.response))

Building Custom Applications

Once integrated, local LLMs work in customer support chatbots, internal knowledge assistants, content generation, code analysis, and countless other applications. The API is identical whether you query OpenAI or your local hardware.

Monitoring and Optimization

Monitor GPU memory usage, inference latency, and throughput. Tools like nvidia-smi show GPU utilization. Adjust batch size, context window, and token generation settings based on monitoring.

If latency is too high, reduce batch size or enable tensor parallelism across multiple GPUs. If memory is exhausted, enable page attention (vLLM handles this automatically) or use smaller models.

When to Choose Local vs Cloud

Choose local deployment when: data is sensitive, volumes are high, cost optimization is critical, or you need model customization. Choose cloud APIs when: you need cutting-edge unreleased models, predictable scaling without hardware investment, or managed support.

Most organizations benefit from hybrid approaches: use local deployment for established models where economics favor it, use cloud APIs for latest models or unpredictable spike workloads.