How to Deploy Your LLM in the Cloud
The simple recipe to choose your GPU and anticipate costs
Serving LLMs in production is mostly an infrastructure problem: latency, throughput, and reliability come down to GPU choice, memory, batching, and the serving runtime.
Most teams start with serverless LLM: you don’t manage servers, you just call an endpoint and pay per use. It’s great for spiky or low-volume traffic because it’s fast to set up and operationally simple. The downside is less control and often less predictable costs at scale (prompt/output lengths, concurrency, very long cold starts, and platform scheduling can all move the bill).
In contrast, self-hosting means you run the model yourself (on your own GPU or a dedicated rented GPU) and operate the serving stack end-to-end. Even if many platforms already serve most models, self-hosting gives you:
Control: pin exact model/version, custom model/adapters, manage rollouts, limits, updates.
Clearer data boundaries: you decide what leaves your system.
Performance + cost ownership: choose the GPU and weight format (bf16/fp16/fp8/fp4/int4), and tune batching/runtime to hit your speed/quality target at the lowest cost. Different model variants and GPUs behave differently, self-hosting lets you debug and optimize precisely.
In this article, I’ll show how to deploy your own LLM on a dedicated GPU using vLLM, an inference engine designed to maximize throughput, while keeping costs under control. We’ll use RunPod (clear per-GPU pricing and easy reproducibility) to deploy an LLM, and test the endpoint with AnythingLLM. I’ll also share practical guidance for choosing the right GPU based on model size, weight format, and whether your workload is likely compute-bound or memory-bandwidth-bound, so you can optimize for both speed and cost.