# Cheat Sheet: Local LLMs in One Page — Local LLMs with Ollama & llama.cpp

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

> Recall the key commands, flags, formulas and defaults for Ollama and llama.cpp at a glance.

## Everything on one page

**Inference**: prefill processes the prompt; decode generates one token per pass using the KV cache; decode speed is bounded by memory bandwidth divided by model size. **Memory**: weights = params x bits / 8; KV cache = 2 x layers x kv_heads x head_dim x context x bytes; add buffers and headroom. **Files**: GGUF holds weights plus metadata and chat template; Q4_K_M is the usual default, Q8_0 near-lossless. **Ollama**: server on `127.0.0.1:11434`; `pull`, `run`, `list`, `ps`, `show`, `stop`, `rm`, `create -f Modelfile`; endpoints `/api/chat`, `/api/generate`, `/api/embed`, OpenAI-style `/v1`; streams NDJSON by default; tuning through `OLLAMA_*` variables. **llama.cpp**: CMake build; `llama-cli`, `llama-server`, `llama-quantize`, `llama-bench`; flags `-m`, `-hf`, `-c`, `-ngl`, `-t`, `-np`, `--jinja`. **Apps**: JSON schemas constrain shape, RAG grounds answers, tools are executed by your code. **Safety**: no built-in auth in Ollama, so never expose the raw port.

## Command crib

The commands from this course in one block.

```bash
ollama pull qwen2.5:7b && ollama run qwen2.5:7b "hi"
ollama ps                                 # memory and CPU/GPU split
ollama create mybot -f Modelfile          # FROM / SYSTEM / PARAMETER
curl localhost:11434/api/chat -d '{"model":"qwen2.5:7b","messages":[{"role":"user","content":"hi"}],"stream":false}'

llama-server -m model.gguf -c 8192 -ngl 99 -np 2 --jinja --port 8080
llama-bench -m model.gguf -ngl 0,99
llama-quantize in-f16.gguf out-Q4_K_M.gguf Q4_K_M
```

## Three numbers to always state

When describing any local setup in an interview or a design doc, state the model and quantization, the context length, and where it runs (full GPU, split, or CPU). Those three explain almost every speed and memory question that follows.

**Quiz:** Which pair correctly matches tool and default address?

- [x] Ollama at 127.0.0.1:11434, llama-server at 127.0.0.1:8080
- [ ] Ollama at 0.0.0.0:8080, llama-server at 127.0.0.1:11434
- [ ] Both on 0.0.0.0:80
- [ ] Ollama at 127.0.0.1:5000, llama-server at 127.0.0.1:3000

*Answer:* Ollama at 127.0.0.1:11434, llama-server at 127.0.0.1:8080. Ollama listens on localhost port 11434 and llama-server on localhost port 8080 by default; both bind to loopback unless configured otherwise.