Self-Host LLM Requirements
What Self-Hosting a Language Model Involves
Self-host LLM requirements are the hardware, software and operational work needed to run a large language model (LLM) on servers you control, instead of calling a provider's API. Three basics are involved: hardware with enough memory, the model weights in a suitable format, and software that loads the model and answers requests.
The model is only one part of the stack. The hardware sets how large a model you can load and how fast it responds. An inference server, meaning the software that runs the model to produce answers, handles incoming requests, groups them and streams replies back to your application.
Hardware: GPU Memory and Model Size
The most important number is the memory on the GPU, called VRAM. The model weights must fit in that memory, and the server needs room for working data during each request. As a rough guide, a model with 16-bit weights needs about 2 GB of memory per billion parameters, so a model with 7 billion parameters needs around 14 GB for its weights alone.
Smaller formats reduce this. Model quantization, which stores weights with fewer bits, can cut the memory needed for weights by half or more, at some cost in quality. Longer prompts also need more memory, because the model keeps intermediate results for every token it has read, and that cache grows with the conversation.
CPUs can run small models, but they generate text much more slowly. For a chatbot where visitors wait for each reply, a GPU is usually the practical choice. The memory question is covered in more depth in VRAM for AI models.
Software, Throughput and Operations
- Inference server: the program that loads the model and serves it over a network interface. Choose one that supports your model format and your GPU.
- Throughput: how many tokens (pieces of words) the system generates per second across all users. One GPU can serve several users, but each reply slows as load rises.
- Latency: the wait before the first word appears, which matters most for chat.
- Operations: updates, monitoring, logs, backups and security patches become your responsibility.
Throughput depends heavily on the model, the hardware and the length of prompts, so benchmark with a workload like your own before you commit to a server.
A Staff Assistant on a Rented GPU Server
A company with a large set of internal documents wants an assistant for its staff. Usage is in the dozens of questions per hour, and the documents must not leave the company.
The team rents a GPU server with enough memory for a mid-sized open model in a compressed format, runs an inference server on it and places the assistant behind the company login. Because usage is modest, one GPU is enough. The team also sets up alerts for memory use and response times, and plans a second server in case the first one fails.
Frequently asked questions
How Much GPU Memory Do I Need to Host an LLM?
It depends mainly on the model size and its numeric format. As a rough guide, a model stored with 16-bit weights needs about 2 GB of memory per billion parameters, plus extra room for longer prompts and the server itself. Compressed formats need less. Check the model's documentation, then leave headroom for peak use.
Is a Self-Hosted LLM Cheaper Than Using an API?
Not always. A self-hosted model has fixed costs for hardware or rented servers, plus staff time for operations, even when nobody is using it. An API charges per request. Self-hosting tends to make sense at steady, high volume or when data must stay in-house, so compare total monthly cost with your real usage, as explained in self-hosted models vs APIs.