Key facts
| Relationship | Ollama builds on the llama.cpp inference engine |
| Model format | Both consume GGUF quantized model files |
| Ollama strength | Model management, CLI, background service, OpenAI-compatible API |
| llama.cpp strength | Granular flags for quantization, context, GPU layers and sampling |
| Hardware | CPU, Metal, CUDA and ROCm support in the engine |
| Server | Both can serve an HTTP API; llama.cpp includes a built-in server |
| Hosted alternative | Plugsky serves 30+ models over one OpenAI-compatible API |
| Endpoint status | Chat, streaming, tools, JSON mode and embeddings are live |
TL;DR
- Ollama is a product built on the llama.cpp engine.
- llama.cpp exposes the tuning knobs; Ollama hides them behind defaults.
- Both use GGUF models, so switching is cheap.
- Ollama wins for a quick local API; llama.cpp wins for optimisation work.
- Either way, keep an OpenAI-compatible client so you can move to a hosted model.
How it works, step by step
- Install Ollama and run your model to establish a baseline.
- Record memory use, latency and quality on your real prompts.
- Install llama.cpp and load the same GGUF model.
- Tune quantization, context size and GPU layers deliberately.
- Compare results against the baseline you recorded.
- Choose the runtime that fits your daily workflow.
- Keep the API surface OpenAI-compatible for portability.
Try it yourself
Open the GGUF size calculator →
What each tool actually is
llama.cpp is the inference engine: it loads quantized GGUF weights and runs them on CPU, Metal, CUDA or ROCm with fine-grained control over how layers are distributed and how sampling works. It also ships a small HTTP server, which is how many other tools expose it.
Ollama is a product around that engine. It adds a model registry, pull commands, a background service, sensible defaults and OpenAI-compatible API routes. The value is not a different inference path; it is less configuration before your first request.
Where they differ in practice
The difference appears when defaults are not enough.
- Quantization control: llama.cpp lets you choose and mix quantization types directly; Ollama pulls predefined model variants.
- GPU offload: llama.cpp exposes explicit layer and split controls, useful for partially fitting a model.
- Context and memory flags: fine-tuning context size and cache behaviour is more direct in llama.cpp.
- Model management: Ollama makes downloading, listing and updating models trivial.
- Server operations: both can serve requests, but Ollama is built to run as a service.
With the same GGUF model and equivalent settings, generation speed is close because the engine is shared.
Choosing and staying portable
Pick Ollama if you want a working local API in minutes and your tasks are well served by standard quantizations. Pick llama.cpp if you are fitting models to tight memory, experimenting with quantization or embedding the engine in another application.
In both cases, keep clients on an OpenAI-compatible surface so the runtime is a configuration detail. When local capacity, concurrency or model size becomes the limit, the same client can call a hosted endpoint. Plugsky serves 30+ models over one OpenAI-compatible API, with chat, streaming, tools, JSON mode and embeddings live and batch endpoints coming soon. See pricing for plans and start free with plugsky-micro and plugsky-lite.
Honest comparison
| Concern | Ollama | llama.cpp | Check before deciding |
|---|---|---|---|
| Setup | One install, one command to run | Build or download, more flags | Time and comfort |
| Control | Sensible defaults | Full control of quantization and offload | Need for tuning |
| Model files | GGUF, pulled by name | GGUF, supplied by path | Model sources |
| Server | Built-in OpenAI-compatible routes | Built-in HTTP server | Client compatibility |
| Best for | Daily local use | Optimisation and embedding in apps | Your workflow |
Frequently asked questions
Is Ollama just llama.cpp?
Ollama uses the llama.cpp engine at its core and adds model management, a background service and API routes. The inference behaviour comes from the engine underneath.
Which is faster?
With the same GGUF model and equivalent settings, generation speed is close because the engine is shared. Differences come from defaults such as context size and the number of GPU layers.
Can I use the same models in both?
Yes. Both consume GGUF files, so the same quantized model usually works in either runtime without conversion.
Do I need to compile llama.cpp myself?
Not always. Prebuilt binaries exist for common platforms, though building from source can be necessary for the latest features or specific accelerators.
Which has a better API?
Both expose HTTP APIs and OpenAI-compatible routes. If your client targets the OpenAI shape, moving between them is a base URL change.
What about GPU offload control?
llama.cpp exposes explicit flags for GPU layers and related settings. Ollama applies defaults tuned for simplicity, which is usually enough but less flexible.
When should I use neither?
When you need concurrent serving, multi-tenant isolation or managed uptime. A GPU serving engine or a hosted OpenAI-compatible endpoint is the better fit.