Build Bigger With Small Ai: Running Small Models Locally

Faster Performance Through Local Execution

Running models locally provides surprising speed advantages. Small models execute faster due to their reduced parameter count - since transformer compute time scales quadratically with parameters, a model with 1 billion parameters runs dramatically faster than one with hundreds of billions. Additionally, eliminating network round trips means zero latency for API calls, making the entire inference process remarkably quick.

Data Privacy and Freedom to Experiment

Local models keep data on your machine, eliminating concerns about sharing sensitive information with cloud providers. This isn't just about privacy paranoia - it liberates developers to experiment freely without worrying about security controls, approval processes, or compliance requirements. Teams can prototype and test ideas without the friction of corporate security reviews.

Cost Structure Benefits

While local models aren't free (you still need hardware), they avoid the per-token pricing of cloud APIs. Modern hardware is increasingly optimized for AI workloads - Intel claims 100 million AI-capable computers will ship within a year, and Apple Silicon dedicates roughly a third of its chip area to neural processing. This hardware investment pays dividends across all your AI experiments without ongoing API costs.

Retrieval Augmented Generation (RAG)

Small models excel when combined with existing factual data through a technique called Retrieval Augmented Generation. Since smaller models may hallucinate when asked about specific facts, RAG compensates by providing relevant data snippets at runtime.

The process involves:

1. Pre-processing your data into a vector store
2. When queried, retrieving relevant data snippets
3. Augmenting the model's prompt with this factual information
4. Getting accurate responses grounded in your actual data

Tools like LlamaIndex and LangChain simplify implementing RAG patterns, allowing models to answer questions about your specific datasets accurately.

Tool Calling and Small Agents

A newer capability enables models to decide when and how to fetch data themselves through "tool calling." Instead of pre-building all the plumbing to connect data sources, the model can:

• Analyze what information it needs
• Write and execute database queries
• Interpret results in context
• Provide natural language responses

For example, newer models like Qwen 2.5 Coder can write SQL queries against DuckDB databases, execute them, and interpret the results - all based on natural language prompts.

Internal Tooling and Back Office Applications

The most successful deployments start with internal tools rather than customer-facing applications:

• IT help desk automation
• Security questionnaire processing
• Data engineering and reporting tasks
• Engineering productivity tools for issue management and code review

Combining Small and Large Models

Small models don't replace cloud-scale models entirely. Like hot and cold data storage, you can use small models for most queries and escalate to larger models when needed. Apple and Microsoft already use this hybrid approach in their AI products.