Open source LLMs: The complete developer's guide to choosing and deploying LLMs

Running open source Large Language Models (LLMs) offers organizations the advantage of avoiding API costs and gaining full control over their AI infrastructure. These models, which include options like Llama 4, DeepSeek-V3, and Qwen 3, provide varied performance and efficiency trade-offs, allowing users to select, deploy, and scale them for production use on their own hardware. Open source LLMs enable complete data control, predictable costs, customization freedom, latency optimization, and freedom from vendor dependencies, making them particularly suitable for industries handling sensitive data. Deploying these models involves choosing the right infrastructure to minimize deployment time, ensuring efficient production scaling with practices like quantization and batching, and leveraging platforms like Northflank to simplify the process. Northflank, for example, offers container-based deployment with automatic GPU provisioning and global availability, allowing even small teams to manage extensive operations without dedicated DevOps resources. The transition from experimentation to production with open source LLMs is now more accessible, thanks to evolving tools and infrastructure, enabling more rapid deployment of sophisticated AI applications.