LLM Deployment and Evaluation

LLM deployment means putting a trained large language model into a real application where users can interact with it, such as inside a mobile app or an AI agent (an autonomous program that performs tasks). The LiteRT-LM engine, for example, lets you deploy the same model file on CPU and GPU, though special compilation is needed for NPU hardware. Deployment matters because an LLM sitting in a research lab has no practical value—it must be integrated into products to be useful.

Evaluation (eval) is the process of testing an LLM’s outputs to measure quality and safety. Instead of just trusting the model’s own judgment, you create labeled datasets and apply standard metrics like recall, precision, and F1 (measures of accuracy and completeness). An eval framework can run thousands of programming assignments to check whether generated code is maintainable and free of tech debt (accumulated design flaws). For AI agents, you also need to evaluate functional performance—whether the agent accomplishes its real-world task—not just technical accuracy. Traces (records of every AI action and LLM call) help you capture what the system did at runtime, making evaluation concrete.

Deployment and evaluation matter for AI development because without proper testing, a deployed LLM might generate poor code, misunderstand your business, or behave unpredictably. Agents are non-deterministic (they produce different outputs each time), so you cannot assume reliability. By evaluating thoroughly before and after deployment, you build workflows that are stable, predictable, easy to improve, and safe.

To deploy an LLM (large language model) step by step, go to a cloud GPU provider like RunPod. Bring your own model code from a GitHub repo that includes a Dockerfile (a configuration file that packages your app). On the RunPod console, select your repo and the LLM you want. The platform handles the underlying GPUs (graphics processing units for fast math) and scaling, so you do not manage hardware. You can also pass environment variables (settings that configure your app) directly.

Deployment does not require a cloud service. You can run an LLM locally on your own machine using tools like Ollama. This eliminates API call costs. For on-device use, Google’s LiteRT-LM engine lets one file deploy across both CPU (central processing unit) and GPU. For specialized neural processing units (NPUs), you need extra compilation.

Evaluation is essential after deployment. Build an eval (a test that checks LLM output) with a simple structure: an input and an expected output. For example, provide a coding task and compare the LLM’s code against a known correct solution. You can use an LLM itself as a judge to compare which agent configuration performed better. Sonar’s framework runs 4,444 Java assignments across models to measure code quality. Always test your agent’s behavior, not just individual LLM responses.

When deploying an LLM (large language model) to production, the biggest mistake is spending more time on infrastructure than on your actual model. RunPod was created because teams found themselves wrestling with GPU (graphics processing unit) setup instead of improving their AI features. To avoid this, use a platform that abstracts hardware management. RunPod lets you bring your code and model, and it handles the rest — you can rent a pod (sandbox virtual environment) on demand, pay by the second, and tear it down when done.

For evaluation, feedback loops are essential. With LLMs you can one-shot tasks and create many variants, letting you experiment with how one feature looks different across users. But if your app only runs one instance at a time, testing many variants creates friction. To evaluate properly, use a simulation platform that runs automated games against your models. Schedule runs and apply Bradley-Terry pairwise ranking to avoid running too many comparisons. This structured evaluation prevents wasted compute on unnecessary tests.

A best practice is to deploy models in a format that works across CPU and GPU with a single file. For NPU (neural processing unit), you need special compilation and get a separate file. Avoid the pitfall of calling a cloud-based LLM to parse your code base when a free local option works fine — you don't always need an API call.

Remember: focus on your prompt and feature, not the hardware. The service should optimize models for the device and take advantage of specific hardware available. Infrastructure is just the foundation; your model's behavior is what matters.