llama.cpp vs Ollama
llama.cpp is free, while Ollama has a free plan plus paid upgrades.
Which one should you try?
llama.cpp
FreeBest for Developers who want direct control over how a local model runs
Ollama
FreemiumBest for Developers who want a local model API without managing model files by hand
We installed and ran both
| Measured | llama.cpp | Ollama |
|---|---|---|
| Server start to first answer, model file not cached (s) | 8.7 | 7.9 |
| Text generation speed, 53-token prompt, relative | 1x | 1.07x |
| Text generation speed, 441-token prompt, relative | 1x | 1.05x |
| Text generation speed, 723-token prompt, relative | 1x | 1.17x |
| Prompt reading speed, 53-token prompt, relative | 1x | 1.06x |
| Prompt reading speed, 441-token prompt, relative | 1x | 1.11x |
| Prompt reading speed, 723-token prompt, relative | 1x | 1.07x |
| Memory used by the model process (GB) | 1.6 | 2.2 |
| Docker image size on disk (GB) | 1.21 | 9.37 |
Same GGUF model file, same 3 cores, measured back to back in one session. The VM's CPU steal was higher during the llama.cpp run (13.6% vs 7.2%), so differences under about 10% are within noise.
Test machine: 8-vCPU AMD EPYC 9354P cloud VM, each test limited to 3 cores and 10 GB RAM, no GPU, no internet (isolated Docker network). Times in seconds are from this CPU-only machine; a desktop with a GPU will be much faster. The VM is shared, so text-generation speed is shown only as a ratio between tools run back to back, not as tokens per second. Docker image size is the on-disk size from docker image ls for every tool. Local AI tools we tested · How we test · raw data
Field notes
All field notes →- Ollama vs llama.cpp: a paired test with the same 3B model
Our controlled pair showed modest generation differences and different process-memory and Docker-image footprints. It did not establish a universal runtime winner.
- How much RAM did our 3B local model actually use?
We observed 1.6–2.2 GB in the model processes running one 3B GGUF. Those numbers are not whole-system RAM requirements or a recommendation for a particular laptop.
Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.
llama.cpp
llama.cpp is the MIT-licensed C/C++ engine that runs GGUF language models on your own hardware and serves them through an OpenAI-compatible API. LocalAI runs models through a backend built on it. We ran its official Docker server with a 3B model in about 1.6 GB of memory on a CPU-only server.
Ollama
Ollama downloads open models and runs them locally behind a command line and API, with optional cloud-hosted models. We ran it on a 3-core CPU server and measured startup, memory and speed.
| Attribute | llama.cpp | Ollama |
|---|---|---|
| Pricing | Free | Freemium |
| Price | — | $20/mo or $200/yr |
| Pricing checked | Not checked yet | Nov 3, 2026 |
| Free trial | Not stated | Not stated |
| Open source | Yes | Yes |
| Has API | Yes | Yes |
| Self-hosted option | Yes | Yes |
| Platforms | Linux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU) | macOS, Windows, Linux, Docker |
| Released | 2023-03 | 2023-06 |
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Made up your mind?
llama.cpp
FreeBest for Developers who want direct control over how a local model runs
Ollama
FreemiumBest for Developers who want a local model API without managing model files by hand
Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.