Running LLMs locally has become a normal part of how developers work. Two tools dominate this space: llama.cpp and Ollama. They look like competitors, but the relationship is more direct — Ollama is built on top of llama.cpp. This post covers the technical differences, where each performs better, and when to use one versus the other.

Table of contents

• 1. The relationship: engine vs. chassis
• 2. Model format: GGUF explained
• 3. Technical comparison
• 4. API compatibility
• 5. Performance & efficiency
• 6. Multimodal support
• 7. Which one should you use?
• 8. Ecosystem & community
• 9. The verdict

1. The relationship: engine vs. chassis

Think of llama.cpp as the engine and Ollama as the car built around it.

• llama.cpp is a C++ library by Georgi Gerganov. It handles the actual LLM math — quantized weight loading, matrix operations, KV cache management — in a single binary with no runtime dependencies. It uses the GGUF file format and runs on everything from a Raspberry Pi to a datacenter GPU.
• Ollama wraps llama.cpp in a management layer: model downloads, versioning, memory scheduling, and an HTTP API. The workflow resembles Docker — pull a named model, run it, get an endpoint.

Ollama is an independent open-source project, not affiliated with the llama.cpp repo.

2. Model format: GGUF explained

Both tools use GGUF (GPT-Generated Unified Format), which replaced the older GGML format. A GGUF file bundles model weights, the tokenizer vocabulary, and metadata into one portable file.

GGUF supports multiple quantization levels, trading accuracy for lower memory use and faster inference:

With Ollama, model downloads are GGUF files fetched from Ollama’s registry and managed automatically. With llama.cpp, you download .gguf files yourself — usually from Hugging Face — and choose the quantization variant directly.

3. Technical comparison

4. API compatibility

Both tools expose OpenAI-compatible HTTP endpoints. Any client that targets the OpenAI API works against either with a URL change.

llama.cpp server:

./llama-server -m model.gguf --port 8080
# http://localhost:8080/v1/chat/completions

Ollama:

ollama serve
# http://localhost:11434/v1/chat/completions  (OpenAI-compatible)
# http://localhost:11434/api/chat             (Ollama native)

The difference is operational: Ollama’s server is persistent and handles model loading, unloading, and request queuing automatically. With llama.cpp you run one process per model and manage the lifecycle yourself — more control, more work.

5. Performance & efficiency

Ollama’s wrapper adds overhead. In raw benchmarks, llama.cpp consistently edges out Ollama in tokens per second and memory use for an equivalent model and quantization level.

• Throughput: llama.cpp lets you tune context window size and batch parameters aggressively. Ollama handles concurrent requests well without manual configuration, but exposes fewer knobs.
• Time-to-first-token: Ollama keeps models warm between requests, so repeated calls are faster. With llama.cpp, you manage model persistence yourself.
• Idle cost: Ollama runs as a background service and uses resources even when nothing is running. llama.cpp only uses resources when the binary executes.
• KV cache: Both expose KV cache tuning, but llama.cpp goes further — --ctx-size for cache size, --flash-attn for flash attention — which matters when you’re working with long contexts.
• Edge: On a Raspberry Pi or embedded device, llama.cpp is the right choice. No runtime dependencies and a small binary footprint.

6. Multimodal support

Both support multimodal models (image + text inputs), but setup differs.

• llama.cpp: Pass a multimodal projector file at startup with --mmproj. Supported architectures include LLaVA, BakLLaVA, and Qwen-VL.
• Ollama: Pull a multimodal model (ollama pull llava) and pass images via the images field in the API. The projector is bundled in the model package — nothing to manage separately.

7. Which one should you use?

Use Ollama if you want to be productive in the next hour. ollama pull llama3 and you have a local endpoint. Switching models is one command instead of a Hugging Face search. Your team gets a consistent environment via a versioned Modelfile. Tool-calling for agents works out of the box.

Use llama.cpp if you’re hitting a wall with what Ollama lets you control. Maybe you need IQ1_M quantization because you’re running on 4 GB of RAM. Maybe you’re linking directly into a Swift app for iOS. Maybe you want to tune the KV cache manually or test a sampling strategy Ollama hasn’t exposed. It’s also the right choice for embedded or air-gapped deployments — no background service, no network calls, just a binary.

8. Ecosystem & community

Ollama’s bet was that most developers just wanted docker pull for models, and it was right. The ecosystem grew around it quickly. llama.cpp is the engine underneath most of that — LM Studio, Jan.ai, and Ollama itself all depend on it, so performance improvements in llama.cpp benefit everything built on top.

9. The verdict

Ollama is where most people should start and probably stay. It has tool-calling, GPU scheduling that works without tuning, and an integration ecosystem that keeps growing. llama.cpp is what you reach for when you’ve hit the edge of what Ollama exposes — unusual hardware, specific quantizations, latency budgets you can’t clear, or embedded deployment.

Start with Ollama. If you hit a wall, go deeper.