head to head · open source

llama.cpp vs openlake

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. openlake has 2,645 stars, 420 forks, 138 open issues and last shipped 2 days ago. llama.cpp leads on adoption by 4,935% (133,181 vs 2,645 stars). llama.cpp is written in C++ under MIT; openlake is written in Rust under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, openlake 16%. llama.cpp was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

llama.cpp ★ 133K openlake ★ 2.6K category AI & Machine Learning

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Side by side

	llama.cpp	openlake
GitHub stars	★ 133K	★ 2.6K
License	MIT	Apache-2.0
Written in	C++	Rust
Last push	2026-11-02	2026-11-01
Forks	⑂ 25K	⑂ 420
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

pick openlake if

You want the openlake feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches Rust
You evaluated both and openlake fits your workflow better

full openlake profile →

About llama.cpp

llama.cpp LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

read the full llama.cpp overview →

About openlake

Fast, easy and efficient storage for LLM Inference and Training 🔥 Distributed storage for GPU workloads. Built on Rust on io uring , OpenLake is a state of the art storage engine delivering million+ iops within 1ms. Updates [2026/09] 🔥 OpenLake tops the MLPerf Storage v3.0 2026 object checkpointing, leading NVIDIA and Nebius (blog). [2026/08] ExANS : a lossless GPU codec for BF16 KV cache: 1.51× cost savings, now available in OpenLake v0.8 (blog). [2026/07] Breaking the KV Wall: managing 100 TB of KV cache and 8× inference throughput with deferred materialization (blog). Older updates [2026/07] Introducing…

read the full openlake overview →

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Frequently asked questions

Is llama.cpp or openlake more popular?

llama.cpp has 133,181 GitHub stars and openlake has 2,645. llama.cpp has the larger community by that measure.

Are llama.cpp and openlake free?

Both are open source. llama.cpp is licensed under MIT and openlake under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and openlake?

llama.cpp is written in C++ and openlake in Rust. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, llama.cpp or openlake?

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose openlake if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.