Mullama vs llama.cpp

Mullama uses llama.cpp as its inference engine. Here's what Mullama adds on top — and when calling llama.cpp directly is still the right answer.

This comparison is a little unusual, because Mullama isn’t an alternative to llama.cpp — it’s built on top of it. The numerical kernel is the same. The GGUF format is the same. The sampler is the same. If you’ve tuned anything on top of llama.cpp, that tuning carries over.

What this page covers is what Mullama adds — and what it doesn’t.

TL;DR

• llama.cpp is the engine. It is a C/C++ library with a CLI (llama-cli), a server binary (llama-server), and a handful of quantization tools. It is excellent at what it does.
• Mullama is what you build on top of that engine when you want a safe Rust API, six language bindings, an Ollama-compatible CLI, two HTTP API surfaces, a Modelfile parser, a Web UI, a TUI, multimodal pipelines, real-time audio, and production observability — without writing all of that yourself.
• If you want the engine and nothing else, use llama.cpp directly. That’s the right call for some projects.
• If you want a polyglot, daemon-or-embedded runtime with batteries included, use Mullama.

Feature matrix

What Mullama adds

A safe Rust API over the C++ library

Calling llama.cpp from Rust today means writing unsafe bindings yourself, or finding a third-party crate and trusting its memory safety. Mullama’s Rust core is a safe, idiomatic API: model loading, context creation, sampling, generation, and embeddings all have real Rust types with Result returns and lifetimes that prevent use-after-free.

That same Rust core is what the other five language bindings (Python, Node.js, Go, PHP, C/C++) sit on top of. So you get one correctness-and-safety story for all of them, instead of N language-specific FFI bindings that each have to get the memory model right.

Six language bindings

llama.cpp exposes a C/C++ API. To use it from anywhere else, you either find a community binding (which exists for Python and a few others, with varying levels of maintenance) or write your own.

Mullama ships first-party bindings for Rust, Python, Node.js, Go, PHP, and C/C++, all on the same core, all behaving identically. The Python binding is not an external project that might lag behind the engine — it’s part of the runtime.

An Ollama-compatible daemon

llama-server exists, but it isn’t the Ollama API. It doesn’t parse Modelfiles. It doesn’t resolve llama3.2:1b to a Hugging Face GGUF. It doesn’t listen on port 11434 by default. It doesn’t ship a model registry.

Mullama wraps the engine in a daemon that does all of those things: same CLI verbs as Ollama (run, pull, serve, chat, etc.), same Modelfile format, same model registry, same HTTP port. If your stack already speaks Ollama, Mullama drops in.

Two HTTP API surfaces

llama-server speaks an OpenAI-compatible API, partially. Mullama speaks two full HTTP surfaces:

• OpenAI-compatible at /v1 — chat completions, embeddings, streaming, tools function calling. Use the official openai SDK without modification.
• Anthropic-compatible at /v1/messages — point the official anthropic SDK at it. llama.cpp does not have this.

Multimodal pipelines

Vision (LLaVA, llava-phi3, Moondream) is supported in llama.cpp. Mullama adds first-class real-time audio: streaming capture, voice activity detection, speech-to-text, and streaming LLM responses, as a pipeline you can drop into a voice-assistant or transcription tool.

Production observability

llama-server is a lean binary. Mullama’s daemon adds the things you tend to want once it’s behind a real service: per-model resource limits, LRU model eviction when memory pressure hits, persistent stats via sled, and a Prometheus metrics endpoint.

A Web UI, a TUI, and an API playground

llama-server has a minimal built-in UI. Mullama ships a more complete Web UI for chat, model management, and an API playground, plus a TUI for terminal-only environments. Both are part of the daemon binary.

When llama.cpp is the right call

llama.cpp is the right answer when:

• You’re building in C or C++ and don’t need anything beyond the engine itself.
• You want the minimum-possible dependency surface — llama.cpp is the engine, and that’s all it is.
• You need the absolute latest engine features the day they’re merged, before they propagate up to a runtime like Mullama or Ollama.
• You’re contributing to inference research and the engine is the layer you want to work in.

Mullama is the right answer when you want everything llama.cpp gives you, plus the runtime layer above it: language bindings, daemon, Ollama CLI compatibility, OpenAI + Anthropic HTTP APIs, Modelfiles, model registry, Web UI, TUI, observability, and multimodal pipelines.

If you’d be wrapping llama.cpp yourself to get any of those things — Mullama is what that wrapper looks like, written once, with safety, and shared across six languages.

Read more

• llama.cpp on GitHub
• Mullama architecture in the docs
• Mullama vs Ollama

Sources: github.com/cognisoc/mullama · docs