Getting Started with Local LLMs in 2025

The landscape of AI has shifted dramatically. While cloud-based APIs from OpenAI, Anthropic, and Google remain powerful, a growing number of organisations are discovering the benefits of running Large Language Models locally. Whether driven by privacy requirements, cost considerations, or the need for offline capability, local LLMs have matured into viable production solutions.

Why Go Local?

Before diving into the technical implementation, let's address the fundamental question: why would you want to run LLMs on your own infrastructure when cloud APIs are so convenient?

For organisations handling sensitive data—healthcare records, financial information, legal documents, or government communications—local deployment isn't just a preference; it's often a requirement.

Choosing Your Model

The open-source LLM ecosystem has exploded with options. Here are the leading contenders as of late 2024:

Llama 3.2 (Meta)

Meta's latest release offers models from 1B to 90B parameters. The 8B and 70B variants hit a sweet spot between capability and resource requirements. Llama 3.2 excels at general-purpose tasks and has strong instruction-following abilities.

Mistral & Mixtral

Mistral AI's models punch above their weight class. The 7B model rivals much larger competitors, while Mixtral 8x7B uses a Mixture of Experts architecture to deliver near-GPT-4 quality at a fraction of the compute cost.

Phi-3 (Microsoft)

Microsoft's Phi-3 family demonstrates that smaller models trained on high-quality data can achieve impressive results. The Phi-3 Mini (3.8B) runs comfortably on modest hardware while handling complex reasoning tasks.

Qwen 2.5 (Alibaba)

Often overlooked in Western markets, Qwen models offer excellent multilingual capabilities and competitive performance across benchmarks.

Hardware Requirements

The hardware you need depends entirely on which model you want to run and at what speed. Here's a practical breakdown:

The "Q4" notation refers to 4-bit quantisation, which reduces memory requirements by roughly 4x with minimal quality loss. For many production use cases, quantised models are indistinguishable from full-precision versions.

Setting Up Your Infrastructure

Let's walk through a practical deployment using Ollama, which has emerged as the simplest way to run local LLMs.

Step 1: Install Ollama

# Linux/macOS
curl -fsSL https://ollama.com/install.sh | sh

# Verify installation
ollama --version

Step 2: Pull Your First Model

# Download Llama 3.2 8B
ollama pull llama3.2

# Or try Mistral 7B
ollama pull mistral

# For a smaller, faster option
ollama pull phi3

Step 3: Run Interactive Chat

ollama run llama3.2

Step 4: Use the API

Ollama exposes an OpenAI-compatible API, making integration straightforward:

curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms"}
    ]
  }'

Production Considerations

Moving from experimentation to production requires addressing several concerns:

Scaling with vLLM

For high-throughput scenarios, vLLM offers superior performance through PagedAttention and continuous batching. It can handle multiple concurrent requests efficiently, making it ideal for API services.

# Install vLLM
pip install vllm

# Start the server
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.2-8B-Instruct \
  --port 8000

Model Serving with TGI

Hugging Face's Text Generation Inference (TGI) provides a production-ready solution with features like token streaming, metrics, and health checks out of the box.

Monitoring and Observability

Track these key metrics for production LLM deployments:

• Tokens per second: Your throughput ceiling
• Time to first token: User-perceived latency
• GPU utilisation: Resource efficiency
• Queue depth: Scaling indicator
• Error rates: Model stability

Cost Comparison

Let's look at real numbers. For an application processing 10 million tokens per month:

The breakeven point typically comes at 3-6 months of operation for dedicated hardware, faster for high-volume applications.

When to Stay in the Cloud

Local LLMs aren't always the answer. Consider cloud APIs when:

• You need the absolute best quality (GPT-4, Claude 3 Opus)
• Your usage is sporadic and unpredictable
• You lack DevOps resources for infrastructure management
• You need capabilities beyond text (vision, real-time voice)
• Rapid model updates are critical to your use case

Getting Started Today

Here's my recommended path for organisations exploring local LLMs:

1. Experiment locally: Install Ollama on a development machine and test various models against your actual use cases
2. Benchmark quality: Compare outputs to your current solution (GPT-4, Claude, etc.) for your specific prompts
3. Measure performance: Test latency and throughput requirements
4. Calculate TCO: Factor in hardware, electricity, maintenance, and opportunity costs
5. Pilot deployment: Start with non-critical workloads before full migration

Conclusion

The open-source LLM ecosystem has reached a maturity level where local deployment is not just viable but often preferable for many enterprise use cases. The combination of improving model quality, decreasing hardware costs, and growing privacy concerns makes this an ideal time to explore self-hosted AI.

The key is matching the solution to your specific requirements. Not every organisation needs to run 70B parameter models on dedicated GPU clusters. Sometimes a well-tuned 7B model running on modest hardware delivers exactly what you need—with complete control over your data.