# llama-server: HTTP, Web UI and Parallel Slots — Local LLMs with Ollama & llama.cpp

Source: https://www.skillbyai.com/en/local-llms/ll-llama-server

> Serve a model with llama-server, call it with curl and configure parallel request slots.

## A lightweight production-style server

`llama-server` loads one model and serves it over HTTP, by default on `127.0.0.1:8080`. Opening that address in a browser gives a built-in chat web UI. For programs it offers OpenAI-compatible routes (`/v1/chat/completions`, `/v1/embeddings` when started in embedding mode, `/v1/models`), its own `/completion` endpoint with llama.cpp-specific options, and `/health` for readiness checks. The server handles concurrency with **slots**: `-np 4` (`--parallel`) creates four slots that are decoded together in batches (**continuous batching**), which raises total throughput when several users are active. The context size given with `-c` is shared among slots, so `-c 16384 -np 4` gives each slot about 4096 tokens (newer builds also offer `--kv-unified`, a shared pool that any slot can fill, so check `--help` on your build). Other useful flags: `--host` to choose the bind address, `--port`, `--api-key` to require a bearer token, and `--jinja` to use the model's embedded chat template, which tool calling needs.

## Start the server and call it

The server must be running before the curl request.

```bash
./build/bin/llama-server -m models/Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  -c 16384 -np 4 -ngl 99 --jinja --port 8080

# In another terminal
curl http://localhost:8080/health
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"Define deadlock in one line."}]}'
```

## Billing counters at a supermarket

Opening four billing counters (slots) lets four customers check out at once, but the store's total floor space (context) is split between the queues. More counters serve more people; each counter has less room for a giant trolley.

## Ollama or llama-server?

Choose Ollama when you want easy model management, automatic loading of many models and a friendly CLI. Choose llama-server when you want one model, explicit control over every flag, the latest llama.cpp features, and a single binary with few moving parts.

**Quiz:** You start llama-server with `-c 8192 -np 4`. Roughly how much context does each slot get?

- [ ] 8192 tokens each
- [ ] 32768 tokens each
- [x] About 2048 tokens each
- [ ] Unlimited

*Answer:* About 2048 tokens each. With the classic per-slot split, the total context is divided among parallel slots: 8192 / 4 = 2048 tokens per slot.