llama.cpp vs LocalAI
Which one should you try?
llama.cpp
FreeBest for Developers who want direct control over how a local model runs
LocalAI
FreeBest for Developers who already use the OpenAI API and want the same calls to run against a local model.
We installed and ran both
| Measured | llama.cpp | LocalAI |
|---|---|---|
| Server start to first answer, model file not cached (s) | 3.5 | 6.4 |
| Text generation speed, 53-token prompt, relative | 0.98x | 1x |
| Text generation speed, 441-token prompt, relative | 0.96x | 1x |
| Text generation speed, 723-token prompt, relative | 0.99x | 1x |
| Prompt reading speed, 53-token prompt, relative | 1.22x | 1x |
| Prompt reading speed, 441-token prompt, relative | 0.99x | 1x |
| Prompt reading speed, 723-token prompt, relative | 0.94x | 1x |
| Memory used by the model process (GB) | 1.6 | 1.7 |
| Docker image size on disk (GB; LocalAI's backend is a separate download) | 1.21 | 1.2 |
Same GGUF model file, same 3 cores, same 4,096-token context, measured back to back in one session; LocalAI runs the model through its own llama-cpp backend. CPU steal: 12.8% during the llama.cpp run, 11.5% during LocalAI's, so differences under about 10% are within noise. LocalAI's image size does not include that backend, a separate 165 MB download. The llama.cpp numbers here come from a second llama.cpp run that day, not the one in the Ollama comparison.
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. How we test · raw data
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.
LocalAI
LocalAI is an MIT-licensed server that exposes an OpenAI-compatible REST API from your own hardware, so existing OpenAI-API code can run against local models. Model engines are separate backends it downloads on demand. We installed v4.11.0 and ran a 3B model in about 1.7 GB of memory on a CPU-only server.
| Attribute | llama.cpp | LocalAI |
|---|---|---|
| Pricing | Free | Free |
| Pricing checked | Not checked yet | Not checked yet |
| 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) | Docker, Linux, macOS, Windows, CPU, NVIDIA GPU, AMD GPU, Intel GPU |
| Released | 2023-03 | 2023 |
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Made up your mind?
llama.cpp
FreeBest for Developers who want direct control over how a local model runs
LocalAI
FreeBest for Developers who already use the OpenAI API and want the same calls to run against a local model.
Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.