Which is the best local LLM runtime in 2026?

It depends on how you want to use it. Mullama is best when you want native in-process bindings for Rust, Python, Node, Go, PHP, or C/C++. Ollama is best when you want a simple HTTP daemon with a great CLI. llama.cpp is best when you want the lowest-level control and the widest hardware support.

Is Mullama a drop-in replacement for Ollama?

Yes, for the CLI, Modelfile format, model registry, and HTTP port (11434). Existing Ollama Modelfiles and client code pointed at localhost:11434 work without modification with Mullama. Mullama also adds Anthropic-compatible HTTP endpoints that Ollama does not have.

Can I switch between these three runtimes without changing my application code?

If your application uses the OpenAI or Anthropic SDK, yes — point the base URL at any of the three. Mullama and Ollama serve on port 11434. llama.cpp's server mode also supports OpenAI-compatible endpoints.

Which one is fastest?

For raw tokens/second, llama.cpp is the lowest layer and usually the fastest because it has the most hardware-specific optimizations. Mullama is a thin Rust wrapper on llama.cpp, so throughput is essentially the same. Ollama adds a daemon layer with a small per-request overhead, usually negligible.

No for any of the three. All three run on CPU. GPU acceleration (CUDA, Metal, ROCm, Vulkan) is opt-in via build flags or environment variables. Apple Silicon uses Metal automatically in all three.

Mullama vs Ollama vs llama.cpp: Which Local LLM Runtime in 2026?

Mullama, Ollama, and llama.cpp are the three most common ways to run a local LLM in 2026. They share an engine (llama.cpp underneath) but differ in how you talk to it. This comparison helps you pick the right one for your use case.

Overview

	Mullama	Ollama	llama.cpp
Language	Rust	Go	C++
Distribution	Daemon, library, or in-process	Daemon	Daemon or library
Native bindings	6 languages (Rust, Python, Node, Go, PHP, C)	HTTP only	C/C++ only
Modelfile format	Ollama-compatible	Original	N/A
HTTP port	11434 (Ollama-compatible)	11434	configurable
OpenAI-compatible API	✓	✓	partial
Anthropic-compatible API	✓	✗	✗
Built-in web UI	✓	✗ (third-party only)	✗
Multimodal (vision + audio)	✓	partial	✓
License	MIT	MIT	MIT

When to use each

Choose Mullama when…

You want to call LLM inference from your application code without running a separate server or making HTTP requests.
You work in multiple languages and want one runtime that speaks them all natively (Rust, Python, Node.js, Go, PHP, C/C++).
You want the same wire-compatible API as Ollama but with the option to embed inference in-process.
You want a built-in web UI and TUI without bolting on Open WebUI or similar.

Choose Ollama when…

You want the simplest possible setup: one command on the CLI, one Modelfile, one HTTP endpoint.
You primarily use the OpenAI Python SDK, LangChain, or LlamaIndex and just need a local backend.
You want a community ecosystem of GUIs (Open WebUI, LibreChat, AnythingLLM) that all assume Ollama’s port 11434.
You do not need to embed inference in your own process.

Choose llama.cpp when…

You want the lowest-level control over every kernel, quantization, and backend flag.
You need the widest hardware support (CUDA, Metal, ROCm, Vulkan, OpenCL, SYCL, RPC, etc.) and want to tune it yourself.
You are porting to a new platform or writing a custom hardware backend.
You are comfortable building from source and managing your own CMake flags.

Code comparison

Same prompt, three ways

Mullama (Python, in-process):

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("Explain GGUF in one paragraph."))

Ollama (HTTP):

# terminal
ollama run llama3.2:1b "Explain GGUF in one paragraph."

# python
import openai
client = openai.OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
print(client.chat.completions.create(
    model="llama3.2:1b",
    messages=[{"role": "user", "content": "Explain GGUF in one paragraph."}],
).choices[0].message.content)

llama.cpp (CLI):

./llama-cli -m llama3.2-1b.gguf -p "Explain GGUF in one paragraph." -n 256

Switching between them

If your application uses the OpenAI SDK, switching between Mullama, Ollama, and llama.cpp is a config change:

# Mullama or Ollama
client = openai.OpenAI(base_url="http://localhost:11434/v1", api_key="not-needed")

# llama.cpp server
client = openai.OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")

Mullama has the advantage of also supporting the Anthropic SDK out of the box (/v1/messages).

Performance

For raw tokens/second on the same model and hardware:

llama.cpp is the baseline (the others are wrappers around it).
Mullama is within 1-3% of llama.cpp because the Rust layer is thin.
Ollama adds 2-5% per-request overhead from the Go daemon, usually negligible for interactive use.

For batch / production serving with batching, the difference widens. Ollama’s batching is less aggressive than vLLM or TGI; llama.cpp’s server has minimal batching. Mullama inherits llama.cpp’s behavior but can be embedded to avoid the HTTP round-trip entirely.

Migration paths

Ollama → Mullama

Drop-in. Same CLI, same Modelfile, same port, same OpenAI-compatible API. Plus Anthropic-compatible API and in-process embedding.

Ollama → llama.cpp

Extract the GGUF model file. Replace ollama run with ./llama-cli -m model.gguf -p "..." for inference. Replace the HTTP daemon with ./llama-server -m model.gguf. Update your client code to point at the new port.

llama.cpp → Mullama

Replace your llama.cpp server invocation with mullama serve --model model.gguf. Same port, same wire format. Your existing OpenAI-compatible client code works unchanged.

Decision matrix

Your situation	Use
Hobbyist, want the simplest setup	Ollama
Python app, need an OpenAI-compatible backend	Ollama or Mullama
Polyglot team (Rust + Python + Node + Go + PHP + C)	Mullama
Need to embed inference in-process, no daemon	Mullama
Need an Anthropic-compatible API	Mullama
Need to run on a 5-year-old GPU with custom kernels	llama.cpp
Need production batch serving at scale	vLLM or TGI, not these three
Need to port to a new platform	llama.cpp
Building a desktop app that needs LLM in-process	Mullama
Building a CLI tool that needs LLM in-process	Mullama
Educational / learning how inference works	llama.cpp + zigllm