LLM Deployment: How to Run Large Language Models in Real-World Systems

When you deploy a large language model, a powerful AI system trained on massive text datasets to understand and generate human-like language. Also known as LLM, it can answer questions, write code, or summarize documents—but only if it runs fast, cheaply, and reliably in production. Most teams skip this step and assume that if a model works in a notebook, it’ll work everywhere. That’s not true. LLM deployment, the process of making a trained language model available for real users through APIs, apps, or internal tools is where things get messy: memory limits, slow responses, unexpected costs, and security holes show up fast.

Running a model isn’t just about picking the right one. It’s about how you handle its KV cache, the temporary memory storing past tokens during inference to speed up responses, which now often takes up more space than the model weights themselves. It’s about choosing between structured pruning, removing entire neurons or layers to reduce size while keeping hardware compatibility and unstructured pruning, removing individual weights for higher compression but requiring special chips to run. It’s about whether you can afford 500ms latency or if your users will bounce at 200ms. And it’s about catching prompt injection, a type of attack where malicious input tricks the model into revealing data or doing unintended actions before your customers get hurt.

You’ll find posts here that break down exactly how companies cut LLM costs by 80% using prompt compression, why FlashAttention slashes memory use, and how QLoRA lets small teams fine-tune models without needing a GPU farm. You’ll see real numbers: how many tokens a 7B model uses per query, how much latency improves with INT8 quantization, and why some teams avoid LLMs entirely for internal tools because the risk isn’t worth it. This isn’t theory. These are the decisions teams are making right now—whether they’re building customer chatbots, automating contract reviews, or running research pipelines. What you’ll find below isn’t a list of tools. It’s a map of what actually works when the pressure’s on.