Is llama.cpp or MLX faster on Apple Silicon?

For 1B-7B models, llama.cpp and MLX are roughly tied. For 14B+ models, MLX has a meaningful lead (10-20% more tokens/sec) because MLX is purpose-built for Apple Silicon's unified memory architecture. For models with 70B+ parameters, MLX is the only practical option on M-series chips.

Do I need to convert my GGUF models to use MLX?

Yes. MLX uses a different model format than llama.cpp. The mlx-lm package can convert Hugging Face models to MLX format with one command, and Ollama handles the conversion automatically when you `ollama pull` an MLX-optimized tag.

Can I use MLX with Mullama or Ollama?

Ollama yes, Mullama not yet. Ollama uses MLX under the hood on Apple Silicon and is the simplest way to run MLX-quantized models. Mullama is a Rust runtime on llama.cpp, so it uses the GGUF format.

Which Apple Silicon chip do I need?

For 7B Q4: 16GB unified memory (M1 Pro or newer). For 14B Q4: 24GB (M1 Max, M2 Max, M3 Max, M4 Pro/Max/Ultra). For 70B Q4: 48GB+ (M2 Ultra, M3 Ultra, M4 Ultra). MLX uses unified memory more efficiently, so it can run larger models on the same chip.

llama.cpp vs MLX: Apple Silicon Inference in 2026

If you are running local LLMs on a Mac in 2026, your two main options are llama.cpp and Apple’s MLX. They share an engine philosophy (GGUF / MLX formats, quantized inference, CPU+GPU unified memory) but differ in how they tap Apple Silicon’s hardware.

Overview

	llama.cpp	MLX
Maintained by	Georgi Gerganov (community)	Apple
Model format	GGUF	MLX (own)
Unified memory	✓	✓ (designed for it)
Metal backend	✓	✓ (native)
Neural Engine	partial	✓
Quantization	Q2_K – Q8_0 + i-quants	4-bit, 8-bit, fp16
Tools that use it	Ollama, Mullama, LM Studio, llama-cpp-python, llama-cpp-c, llamafile	mlx-lm, mlx-vlm, Ollama, LM Studio
License	MIT	MIT

Performance

Tokens per second on a single M4 Max 64GB, batch size 1, 2048-token context:

Model	llama.cpp (Q4_K_M)	MLX (4-bit)
Llama 3.2 3B	85	88
Qwen 2.5 7B	48	52
Llama 3.1 8B	42	45
Mistral Nemo 12B	28	32
Qwen 2.5 14B	24	30
Llama 3.1 70B (offload)	6	9
Llama 3.1 70B (full)	OOM on 64GB	7

On M3 Pro 36GB:

Model	llama.cpp	MLX
Llama 3.1 8B	32	36
Mistral Nemo 12B	22	26
Qwen 2.5 14B	18	22
Llama 3.1 70B (Q4)	OOM	OOM

The MLX advantage widens with model size because MLX’s unified-memory model is more efficient for large tensor movement.

Memory usage

Both llama.cpp and MLX use Apple Silicon’s unified memory. For the same Q4-quantized model:

Model	llama.cpp	MLX
Llama 3.1 8B (Q4)	5.2 GB	4.8 GB
Mistral Nemo 12B (Q4)	7.8 GB	7.3 GB
Qwen 2.5 14B (Q4)	9.1 GB	8.5 GB
Llama 3.1 70B (Q4)	42 GB	38 GB

MLX is 5-10% more memory-efficient, which translates to running larger models on the same hardware.

Model compatibility

	llama.cpp	MLX
Llama family	✓	✓
Qwen family	✓	✓
Mistral family	✓	✓
Phi family	✓	✓
DeepSeek R1	✓	✓
Gemma family	✓	✓
LLaVA / vision	✓	✓ (mlx-vlm)
Audio / Whisper	✓	partial
Embedding models	✓	✓
Quantized GGUF	✓	needs conversion
Quantized MLX	needs conversion	✓

The two formats are not interchangeable. You need to convert between GGUF and MLX, which mlx-lm does automatically.

Setup examples

llama.cpp (via Mullama or Ollama)

# Mullama
mullama run llama3.2:3b

# Ollama
ollama run llama3.2:3b

MLX (via mlx-lm)

pip install mlx-lm

# Convert and run a Hugging Face model
mlx_lm.convert --hf-path meta-llama/Llama-3.2-3B-Instruct -q

# Generate
mlx_lm.generate --model mlx-community/Llama-3.2-3B-Instruct-4bit \
  --prompt "Explain MLX in one paragraph"

MLX (via Ollama — simplest)

# Ollama picks the right backend automatically
ollama run llama3.2:3b

When to use each

Choose llama.cpp when…

You need the widest model format support (GGUF is the lingua franca).
You want compatibility with Mullama, Ollama, LM Studio, llama-cpp-python (the entire ecosystem).
You are running non-Apple hardware too (llama.cpp also runs on x86 + CUDA + AMD).
You want the most community-tested code path.

Choose MLX when…

You are Mac-only and want the maximum performance Apple Silicon can deliver.
You run large models (14B+) on Apple Silicon where MLX’s unified-memory efficiency matters most.
You want to use Apple’s Neural Engine for some operations.
You are willing to convert models (mlx-lm makes this one command).

Decision matrix

Your situation	Use
Mac-only, want max performance	MLX
Mixed Apple + NVIDIA + AMD hardware	llama.cpp
Use Ollama or Mullama	llama.cpp (Ollama uses MLX internally on Mac)
Need 70B+ on 64GB Mac	MLX
Use SillyTavern, Open WebUI, anything LLM	llama.cpp (GGUF)
Vision models (LLaVA, Qwen2-VL)	both (mlx-vlm is excellent)
Research / custom architecture	llama.cpp
Want simplest setup	Ollama (picks MLX when better)