If you run large language models on your own PC, you will keep running into two names: llama.cpp and Ollama. They are closely related. Ollama's own README lists llama.cpp, "founded by Georgi Gerganov", under its supported backends. So the choice is less about which is faster on paper and more about how much control you want versus how much convenience.
Forge PC has not benchmarked the two against each other, and we do not quote speed comparisons. This comparison is based on the official llama.cpp repository and server documentation and Ollama's official documentation and README, read on 13 November 2026. Both projects change quickly, so check the current docs before you rely on a specific command.
The short version
| llama.cpp | Ollama |
|---|
What it is | A C/C++ inference engine, command-line tools and a server | An app and service for downloading and running models |
Licence | MIT | MIT |
Windows install | Installer script, pre-built binaries, or build from source | Native Windows app; no administrator rights needed |
Models | GGUF files, including straight from Hugging Face | Ollama's model library, plus imported models |
GPU support | CUDA (NVIDIA), HIP (AMD), SYCL (Intel), Vulkan, Metal and more | NVIDIA and AMD Radeon on Windows (ROCm or Vulkan) |
Partial GPU offload | Yes, you can set how many layers go in VRAM | Managed by Ollama |
API | Built-in server with web UI and OpenAI-compatible routes | Local API on port 11434, plus OpenAI compatibility |
Best for | Tinkerers who want control and the newest features | Most people who want local AI that just works |
Setup
Ollama is the easier start. Its Windows documentation says it runs as a native application, installs into your home folder without administrator rights, and then runs in the background with the ollama command available in any terminal. You need at least 4GB of disk space for the install, plus space for models, which Ollama warns "can be tens to hundreds of GB in size". Then ollama run with a model name downloads and starts it. Our guide to running AI models locally walks through it.
llama.cpp now has a much friendlier quick start than it used to. Its README offers an installer script for PowerShell and Linux, pre-built binaries on the releases page, Docker images, or building from source. Once installed, the README's examples download a GGUF model straight from Hugging Face and either chat with it on the command line or launch an OpenAI-compatible server. You still pick the model file and quantisation yourself, which is more work but more transparent. Our GGUF quantisation explainer helps with that choice.
Hardware support
This is where llama.cpp has a clear edge. Its README lists backends for CUDA on NVIDIA GPUs, HIP for AMD, SYCL for Intel GPUs, Vulkan for a wide range of GPUs, and Apple's Metal, plus optimised CPU paths (AVX, AVX2, AVX-512 and AMX on x86). It can also run "CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity", and the server lets you set how many layers to keep in VRAM.
Ollama's Windows docs list support for NVIDIA cards (driver 551.61 or newer) and AMD Radeon cards using either a ROCm v7-capable driver or a Vulkan-capable driver. It notes some Radeon RX 6000-series systems may not expose ROCm v7 on current Windows drivers and recommends Vulkan for those. If you use an Intel Arc card, check Ollama's current hardware list; llama.cpp's SYCL and Vulkan backends are the more established routes for Intel GPUs.
For choosing a card, see NVIDIA vs AMD for local AI and our best GPU for local LLMs.
Control and defaults
Ollama makes sensible choices for you, but you should know what they are. Its FAQ says it uses a 4,096-token context window by default, which you can raise with the OLLAMA_CONTEXT_LENGTH environment variable. If long documents or chats seem to "forget" earlier text, that default is a common reason.
llama.cpp exposes almost everything as a command-line flag: context size, GPU layers, sampling settings and much more. The server documentation runs to many pages of options. That is overkill for casual use, but invaluable if you are squeezing a large model into limited VRAM or testing a brand-new model architecture.
APIs and apps
Both work as a local back end for other apps.
Ollama serves its API on 127.0.0.1:11434 by default, and apps such as AnythingLLM can detect it automatically. Its README lists dozens of community integrations, including AnythingLLM. We cover Ollama's OpenAI-compatible API and Open WebUI with Ollama.
llama.cpp's llama-server provides "OpenAI API compatible chat completions, responses, and embeddings routes" and its own web UI, according to its documentation.
If you want a graphical app rather than either, LM Studio is the other popular choice.
Which should you choose?
Choose Ollama if you want local AI running in minutes, use an NVIDIA or recent AMD card, and want other apps to find your models automatically.
Choose llama.cpp if you want the widest hardware support (including Intel Arc via SYCL or Vulkan), fine control over context, offloading and quantisation, or access to new model support as soon as it lands upstream.
Many people end up using both: Ollama for everyday use, and llama.cpp when they need a setting Ollama does not expose.
Our verdict
For most people, Ollama is the better starting point: a native Windows install, automatic model downloads and a local API that other apps already understand. llama.cpp is the better tool if you want maximum control, broader GPU support or the newest features first. Both are MIT-licensed and free. We have not benchmarked them; the comparison comes from the official llama.cpp and Ollama documentation.
