The "Large" in Large Language Models
Serving a random forest or a small neural network is relatively straightforward: wrap it in an API, put it on a standard server, and you're done. The model fits in a few hundred megabytes of RAM, responds in milliseconds, and scales horizontally like any other web service.
LLM Ops is a different discipline entirely, because Large Language Models are, well, large. A model like Llama-3-70B has 70 billion parameters — numerical weights that encode everything the model "knows." Just loading those weights into memory requires over 140 GB of High-Performance Video RAM (VRAM). That doesn't fit on a standard server. It requires expensive, specialized GPU servers (like NVIDIA A100s or H100s), and even then you may need multiple GPUs working together before the model can process a single request.
The Three Pillars of LLM Ops
Managing these models in production requires solving three major challenges simultaneously:
- Hosting Efficiently: How do we fit giant models onto available hardware without going bankrupt, and serve requests fast enough to be useful?
- Token Economics: Understanding the cost structure. How much does it cost to ask a question versus generate an answer? How do you control runaway costs at scale?
- Efficient Customization: How do we teach the model new domain-specific behaviors without retraining all 70 billion parameters from scratch?
These three pillars interact. A poorly optimized serving setup inflates your token costs. A naive customization strategy forces you to store multiple full-size model copies. LLM Ops is the discipline of solving all three together.