Ollama vs llama.cpp: the same engine underneath, different control on top

Ollama vs llama.cpp is less a contest than a choice of layer. Ollama's own README lists llama.cpp as its supported backend: llama.cpp is the C/C++ inference engine that runs the model on your CPU or GPU, and Ollama wraps an engine like it in a model library, a background service and a simple API. So the useful question is not which one is faster in the abstract, but whether you want the convenience of Ollama or the direct control of llama.cpp. This guide compares the two from their documentation as of October 10, 2026, shows the defaults that explain most "Ollama is slower" reports, and says when a third option, vLLM, makes more sense.

Ollama vs llama.cpp at a glance

Table: Ollama and llama.cpp compared from their own documentation (October 10, 2026)

Both run the same GGUF model files, and both can split a model between GPU and CPU when it does not fit in video memory. If you have not run a local model before, start with our guide to running an LLM locally.

How Ollama and llama.cpp are related

llama.cpp describes its goal as running LLMs "with minimal setup and state-of-the-art performance on a wide range of hardware". It is plain C/C++ without dependencies, built on the ggml library, and ships backends for NVIDIA (CUDA), AMD (HIP), Apple silicon (Metal), Vulkan, Intel GPUs (SYCL) and more, with Intel NPU support through OpenVINO marked as in progress. It also brings its own tools: a chat in the terminal, a server and a quantizer.

Ollama sits one level up. Its README says "Start building with open models", lists llama.cpp under "Supported backends", and adds what llama.cpp leaves to you: a curated library of models with tags, a background service that loads a model when a request arrives and unloads it later, a Modelfile format for custom settings, a desktop app, and one-command connections to coding tools such as Claude Code, Codex and Copilot. Our Ollama commands guide covers the everyday commands.

Two practical consequences. First, a model that works in one usually works in the other, because both read GGUF. Ollama's import guide even sends you to llama.cpp's llama-quantize to prepare a GGUF file, since Ollama does not quantize GGUF files on import. Second, the two projects release separately, so on a given day they may not run the same version of the underlying code.

Is llama.cpp faster than Ollama?

We have not benchmarked them, and the third-party numbers online disagree with each other. What the documentation does show is that most speed and memory differences people report come from defaults, not from a different engine:

• Context length. Ollama uses a 4,096-token context window unless you set OLLAMA_CONTEXT_LENGTH or num_ctx. llama-server takes the context length from the model file, which for recent models can be hundreds of thousands of tokens and needs far more memory. A small context is faster and lighter but silently cuts long prompts; a large one can push layers off the GPU. Our VRAM guide explains why the context costs memory.
• Parallel requests. Ollama processes one request at a time per model by default (OLLAMA_NUM_PARALLEL=1), and its FAQ notes that memory grows with parallel requests times context length. llama-server picks the number of slots automatically and keeps continuous batching on, which helps when several clients call it at once.
• GPU layers and flash attention. llama.cpp decides how many layers go to the GPU by default (-ngl auto) and sets flash attention to auto; Ollama makes the split itself and shows it in ollama ps. If a model ends up partly on the CPU, generation slows sharply in either tool; Ollama not using your GPU shows how to check with ollama ps.
• Quantization. Comparing a Q4 file in one tool with a Q8 file in the other measures the files, not the tools. Our explainer on quantization covers what each level costs in quality and memory.

To compare fairly, run the same GGUF file with the same context length, number of GPU layers and parallel setting, and measure tokens per second on your own prompts.

API, models and the commands side by side

Starting a model. In Ollama, ollama run downloads a model from its library if needed and opens a chat. In llama.cpp, the README's quick start is llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF for a chat and llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF to launch an OpenAI-compatible server, both pulling the model straight from Hugging Face.

The API. Ollama listens on http://localhost:11434 with its own API and OpenAI-compatible routes for chat completions, responses, embeddings and models, plus an Anthropic-compatible one. llama-server offers OpenAI-compatible chat completions, responses and embeddings, Anthropic Messages compatibility, reranking, schema-constrained JSON, function calling "for ~any model", speculative decoding and a built-in web UI. Note the port: the server's current documentation gives 9931 as the default, while many older guides and its own Docker examples use 8080, so set --port explicitly if a client expects one or the other.

Several models. Ollama loads up to three models per GPU by default (three on CPU) if they fit in memory, and unloads each after five minutes without use. Started without a model, llama-server runs in router mode: it finds models in its cache or in a folder you pass with --models-dir, loads them on demand and keeps up to four at once by default.

Security. Both listen only on your own machine by default (127.0.0.1); Ollama changes that with OLLAMA_HOST and llama.cpp with --host. llama-server can require API keys with --api-key; if you expose either tool to a network, put authentication in front of it.

Which one to use, and when vLLM is the better choice

• Choose Ollama if you want models running in minutes, switch between them often, or connect local models to apps and coding tools that already speak its API. Compare it with the desktop alternative in Ollama vs LM Studio.
• Choose llama.cpp if you want every setting in your hands (context, batch sizes, GPU layers, speculative decoding), more hardware backends (its README lists 17, from CUDA and Metal to SYCL and Snapdragon), a server that handles several users with continuous batching, or a single binary without a background service.
• Choose vLLM if you serve many users from one or more data-center GPUs. Its README lists "state-of-the-art serving throughput" with PagedAttention memory management, continuous batching and prefix caching, an OpenAI-compatible server with Anthropic Messages support, and support for NVIDIA, AMD and Intel GPUs and CPUs, with Apple silicon through a plugin. It is built for throughput on servers, not for a single person on a laptop.

Whatever you pick, the hardware decides more than the software: see our best GPU for local LLMs guide before buying anything.