mullama vs Ollama vs vLLM: Choosing a Local LLM Server in 2026

The question

Should I use Ollama, vLLM, LocalAI, LM Studio, or mullama to serve LLMs locally?

Local LLM serving has split into distinct categories in 2025–2026. The right answer depends on whether you are prototyping, shipping to production, or doing research. This post is the comparison we wish we had when we started mullama.

The 60-second version: Ollama is the default for getting started; vLLM is the production-grade GPU server; LM Studio is the desktop GUI; LocalAI is the OpenAI-compatible gateway over several backends; mullama is the Ollama-compatible runtime you can also embed in-process, from six languages, without a daemon.

What each option is

mullama is a local LLM runtime built on llama.cpp. Its core is a safe Rust API over the llama.cpp library, with native bindings for Python, Node.js, Go, PHP and C/C++, so inference can run inside your process with no daemon and no HTTP. It can also run as a server with the same CLI verbs as Ollama (run, pull, serve, list, ps, create, show, rm, cp), the same Modelfile format and the same port (11434), exposing OpenAI-compatible and Anthropic-compatible HTTP APIs.

Ollama is a high-level wrapper around llama.cpp with a download-and-run UX (ollama run llama3), an OpenAI-compatible REST API, and a Go-based model manager. The de-facto choice for getting started.

vLLM is a production-grade GPU inference server that began at UC Berkeley. It uses PagedAttention for KV-cache memory management, with continuous batching and tensor parallelism. The right choice for high-QPS production serving.

LocalAI is an OpenAI-compatible REST API that can run llama.cpp and other backends behind it. A reasonable choice for a drop-in OpenAI API replacement with a choice of engine per model.

LM Studio is a desktop application for running LLMs locally, with a GUI and a local OpenAI-compatible server. Closed source, but excellent for exploration and for non-developers.

The dimensions that matter

Dimension	mullama	Ollama	vLLM	LocalAI	LM Studio
Primary goal	Embeddable runtime + Ollama-compatible server	Developer experience	Production GPU serving	OpenAI-compatible API over many backends	Desktop app
Engine	llama.cpp (safe Rust core)	llama.cpp	Own engine (PagedAttention)	Multiple backends	llama.cpp / MLX
Model format	GGUF	GGUF	Hugging Face weights (safetensors)	Backend-dependent	GGUF / MLX
In-process embedding	Yes: Rust, Python, Node.js, Go, PHP, C/C++	No (HTTP)	No (server)	No (server)	No (app)
HTTP API	OpenAI- and Anthropic-compatible	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible local server
Ollama CLI / Modelfile compatible	Yes	Yes	No	No	No
Default platform	CPU + GPU	CPU + GPU	GPU	CPU + GPU	CPU + GPU + MLX
Multi-model serving	Yes, with LRU eviction	Yes, load on demand	One base model per server; LoRA adapters	Yes	Yes
Observability	Prometheus metrics, persistent stats	Logs	Prometheus metrics	Logs	GUI
Production users	(early)	Many	Many	Many	(consumer)
Licence	MIT	MIT	Apache-2.0	MIT	Proprietary

When to use which

Use mullama when:

You want local inference as a library in the language your application is already written in, without running a daemon or spawning a Python subprocess.
You want an Ollama-compatible server (same commands, Modelfiles and port) but also want the option to move inference in-process later.
You need an Anthropic-compatible API against a local model.
You are building a desktop, edge or CLI application where an extra service to supervise is a real cost.

Use Ollama when:

You want the easiest path from ollama run llama3 to a working API, with the largest community behind it.
You are prototyping, and the default model parameters are good enough.
You need a wide model registry with pre-quantised downloads.

Use vLLM when:

You are serving a high-QPS production workload on data-centre GPUs.
You need PagedAttention, continuous batching, and tensor parallelism.
You have a team that can run a GPU inference service in production.

Use LocalAI when:

You need a drop-in OpenAI-compatible API and want to choose the backend per model.
You are migrating from OpenAI and want to keep your client code unchanged.

Use LM Studio when:

You are exploring, not shipping.
You are a non-developer who wants a GUI.
You are on Apple Silicon and want the MLX backend.

Why might you pick mullama?

The honest answer: most teams shouldn’t need to think about it. If you are shipping a high-throughput production LLM service, vLLM is the right answer. If you are prototyping, Ollama is the right answer. mullama is for a specific audience:

Application developers outside Python who want a local model as a dependency rather than a service: a Node.js, Go or PHP application, or an Electron or Tauri desktop app linking the C ABI directly.
Teams already invested in Ollama who want the same commands and Modelfiles but with an in-process option and an Anthropic-compatible endpoint.
Edge deployments where a quantised model embedded in the application, with no network dependency and no daemon, is the whole point.

mullama publishes no throughput or latency benchmark against Ollama or vLLM, and we do not claim one here. If the choice turns on performance, measure tokens per second and time to first token on your own hardware, models and quantisation.

A 10-minute mullama eval

# Install the CLI and server
curl -fsSL https://mullama.cognisoc.com/install.sh | sh

# Run a model; the daemon starts in the background
mullama run llama3.2:1b "What is the capital of France?"

# Start an OpenAI-compatible HTTP server on port 11434
mullama serve --model llama3.2:1b

# Point any OpenAI-compatible client at http://localhost:11434/v1

And the in-process path, with no server at all:

# pip install mullama
from mullama import Model, Context
model = Model.load("llama3.2-1b.gguf", n_gpu_layers=32)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!"))

If both paths give you what you need, the server is the migration path from Ollama and the library is the reason to migrate.