MLX vs. llama.cpp on M-series Macs: who wins in 2026 ?

#The two camps in 2026

MLX was released in late 2023 by Apple’s ML team. It is a Python framework that resembles a fusion of NumPy and PyTorch, designed from the start for unified memory and Metal. Its scope is broad: LLMs, vision, audio, and fine-tuning. The mlx-lm package specifically handles language-model inference and training.

llama.cpp is a C++ project started in 2023 that became the de facto standard for local LLM inference across platforms. On Mac, its Metal backend generates compute shaders to use the Apple Silicon GPU. This is what powers Ollama, LM Studio, and most consumer-facing tools. Model format: GGUF.

#1. Installation

On the MLX side, everything goes through pip. The mlx-lm runtime handles downloading from Hugging Face, conversion, quantization, and inference.

pip install mlx-lm

# Premier test : Qwen 3.5 4B 4-bit en une commande
mlx_lm.generate --model mlx-community/Qwen3.5-4B-Instruct-4bit \
  --prompt "Explique la mémoire unifiée Apple en 3 phrases."

With llama.cpp, you have three options: build from source with Metal enabled, use Homebrew, or use a wrapper such as Ollama or LM Studio. For an honest comparison, we use the compiled binary.

brew install llama.cpp

# Inférence sur un modèle GGUF déjà téléchargé
llama-cli -m ./Qwen3.5-4B-Instruct-Q4_K_M.gguf \
  -p "Explique la mémoire unifiée Apple en 3 phrases." \
  -n 256 -ngl 99

#2. Tokens/sec on M3 Max and M4 Pro

Here are representative ballpark figures for two common machines in 2026: a MacBook Pro M3 Max 64 GB (400 GB/s bandwidth) and a Mac mini M4 Pro 48 GB (273 GB/s). Models compared at equivalent quantization—4-bit MLX vs Q4_K_M GGUF—with a 200-token prompt and 256 generated tokens.

Qwen 3.5 4B — M3 Max

MLX 4-bit: 150 tok/s · llama.cpp Q4_K_M: 135 tok/s. MLX is ahead by ~11%.

Qwen 3.5 9B — M3 Max

MLX 4-bit: 72 tok/s · llama.cpp Q4_K_M: 66 tok/s. ~9% gap in MLX’s favor.

Gemma 4 12B — M3 Max

MLX 4-bit: 48 tok/s · llama.cpp Q4_K_M: 44 tok/s. MLX +9%.

Mistral Small 24B — M3 Max

MLX 4-bit: 24 tok/s · llama.cpp Q4_K_M: 22 tok/s. MLX +9%.

Qwen 3.8 27B — M3 Max

MLX 4-bit: 21 tok/s · llama.cpp Q4_K_M: 19 tok/s. MLX +10%.

Qwen 3.5 9B — M4 Pro

MLX 4-bit: 50 tok/s · llama.cpp Q4_K_M: 46 tok/s. MLX +9%.

The pattern is clear and reproducible: MLX gains between 8 and 15% on pure generation. The difference comes from the fact that MLX's Metal kernels are written and optimized directly by Apple, with detailed knowledge of the GPU scheduler and L1/L2 caches. llama.cpp also uses Metal kernels, but they are more generic.

#3. Hugging Face model support

This is one of the areas where the two ecosystems differ in practice. MLX has its own weight format (.safetensors with MLX config), while llama.cpp uses GGUF.

MLX — availability

The mlx-community organization on Hugging Face publishes most popular models (Qwen 3.5/3.8, Gemma 4, Mistral, Granite 4.2) in 4-bit and 8-bit versions, often within a week of release.

MLX — uncommon models

For an unusual fine-tune or a confidential model, you’ll need to convert it yourself with mlx_lm.convert. Typical conversion: 2–10 minutes depending on size.

llama.cpp — availability

GGUF files are everywhere. Bartowski, TheBloke (archives), Unsloth, and official publishers release GGUF versions even before MLX for many models.

llama.cpp — very recent models

When a new architecture is released (e.g., a novel MoE or exotic attention), llama.cpp has to implement it in C++. Typical delay: a few days to 2 weeks. Because MLX uses Python + Metal, it sometimes catches up faster when Apple has already prepared it.

mlx_lm.convert \
  --hf-path Qwen/Qwen3.5-9B-Instruct \
  --mlx-path ./qwen3.5-9b-mlx-4bit \
  -q --q-bits 4 --q-group-size 64

#4. Available quantizations

This is probably the area where llama.cpp dominates decisively. GGUF offers about a dozen quantization variants (Q2_K, Q3_K_S/M/L, Q4_K_S/M, Q5_K_M, Q6_K, Q8_0, plus the I-quants IQ2_XXS to IQ4_NL) that let you finely tune the size/quality tradeoff.

MLX

4-bit, 6-bit, and 8-bit quantization. Main parameter: group-size (32, 64, 128). No direct equivalent of mixed K-quants (Q4_K_M preserves more precision in certain layers).

llama.cpp

GGUF Q4_K_M (recommended), Q5_K_M, Q6_K, Q8_0, FP16, plus the I-quants for going even lower. Calibration is possible via imatrix.

Observed quality

At the same size, GGUF Q4_K_M and MLX 4-bit deliver very similar perplexity (difference < 1%). To fit a large model into limited RAM (Qwen 3.8 27B on 16 GB), llama.cpp's I-quants remain more granular.

#5. Unified memory: who makes the best use of it?

On M-series Macs, the CPU and GPU share the same RAM. No copying, no PCIe transfer, just one shared pool. That's the structural advantage of Apple chips for LLM inference, and both frameworks benefit from it—but not in the same way.

MLX

Designed natively for unified memory. Tensors live in an addressable space shared by the CPU and GPU without distinction. Zero-copy conversion between NumPy and MLX. This is the major architectural argument for Apple.

llama.cpp Metal

Allocates shared MTLBuffers. It works, but adds an abstraction layer. For models that exceed the default allocated VRAM, you may need to manually adjust the limit with sudo sysctl iogpu.wired_limit_mb.

Large models on 64 GB

In 2026, even the general-purpose flagship Qwen 3.8 27B weighs only ~18 GB in 4-bit: 64 GB of unified RAM leaves plenty of room for a large MoE such as Qwen 3.6 35B-A3B (~23 GB), or for the same model in Q8 for maximum quality. With 128 GB (top-spec M3 Max or M2 Ultra), you can load multiple models in parallel without compromise.

#6. Python integration

If you’re coding an agent, a RAG pipeline, or instrumenting your LLM with LangChain / LlamaIndex / your own code, Python experience matters as much as tokens/sec.

from mlx_lm import load, stream_generate

model, tokenizer = load("mlx-community/Qwen3.5-9B-Instruct-4bit")

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Résume MLX en 3 phrases."}],
    tokenize=False, add_generation_prompt=True,
)

for chunk in stream_generate(model, tokenizer, prompt, max_tokens=256):
    print(chunk.text, end="", flush=True)

On the llama.cpp side, the Python integration uses llama-cpp-python (official bindings). The API is more verbose and closer to C++, but it is OpenAI-compatible once the server is running.

from llama_cpp import Llama

llm = Llama(
    model_path="./Qwen3.5-9B-Instruct-Q4_K_M.gguf",
    n_gpu_layers=-1,   # tout sur Metal
    n_ctx=8192,
    flash_attn=True,
)

for chunk in llm.create_chat_completion(
    messages=[{"role": "user", "content": "Résume llama.cpp en 3 phrases."}],
    stream=True,
):
    delta = chunk["choices"][0]["delta"].get("content", "")
    print(delta, end="", flush=True)

MLX — convenience

Very clean API, NumPy-like syntax, native HF Hub integration. For a data scientist, it's immediate.

MLX — limitations

There is not yet an official integrated OpenAI-compatible endpoint. To serve a model to multiple clients, you need to build your own FastAPI wrapper.

llama.cpp — convenience

The Python API is adequate, but real production use goes through llama-server (binary), which natively exposes /v1/chat/completions OpenAI-compatible.

llama.cpp — limitations

The llama-cpp-python wheel must be rebuilt with the correct Metal flag (CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python --force-reinstall --no-cache-dir). A source of friction.

#7. Ecosystem and tools

Beyond raw runtime, the ecosystem determines what you can actually do.

Ollama

Runs on llama.cpp. The simplest way to run an LLM on a Mac with an OpenAI-compatible API at localhost:11434.

LM Studio

Supports both engines since 2025: llama.cpp by default, MLX as an option for compatible models. Switch with one click.

LoRA fine-tuning

MLX has native mlx_lm.lora, which works cleanly on the integrated GPU without hacks. llama.cpp does not support fine-tuning—you need to use Unsloth or MLX.

Serve a model

llama.cpp wins hands down over llama-server: multi-client support, batching, slots, and OAI compatibility. On the MLX side, mlx_lm.server has existed since 2025 but remains basic.

Vision and audio

MLX has well-maintained extensions (mlx-vlm, mlx-whisper). llama.cpp supports vision through LLaVA / Qwen-VL models, with support depending on the build.

#Verdict by use case

You want the maximum tokens/sec in local chat

MLX. +10% free, and the gap widens on larger models. Especially in 4-bit.

You want to serve an endpoint to multiple clients (team, app)

llama.cpp (llama-server or Ollama). Multi-slot, batched, OpenAI-compatible, and stable for a long time.

You test around ten different models per week

llama.cpp. The GGUF ecosystem is unmatched in quantization coverage and freshness.

You are coding a Python project (agent, RAG, pipeline)

MLX if everything is local and Mac-only. llama-cpp-python or a Ollama client if you want portable Linux/Mac code.

You want to fine-tune a 7-13B model on your Mac

MLX. mlx_lm.lora works very well on M3 Max / M4 Pro with 32 GB+.

You're just getting started and only want an LLM that works

Ollama (therefore llama.cpp). One command and you're done.

#Go further

A few related reads to take the topic further: