Best Local LLM on Mac M4 Pro 48GB: Setup, Quantization and Real Speed

A 48GB M4 Pro Mac mini fits Llama 3.3 70B at Q4 quantization, about 48GB, right at the ceiling, and runs Ornith 1.0 35B or Gemma 3 27B with real headroom. Here is how to install both paths, which format to pick, and what's actually measured versus what isn't for this exact chip.

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A 48GB M4 Pro Mac mini is the entry point for running Llama 3.3 70B locally: the model needs about 48GB at Q4 quantization, which fits with almost no headroom left over. For a faster daily driver on the same machine, Ornith 1.0 35B (about 20GB) or Gemma 3 27B (about 15GB) leave far more unified memory free and generate noticeably faster.

Written by Priya Raghunathan, local-AI hardware reviewer. I test consumer GPUs and Apple Silicon against real llama.cpp and MLX benchmarks before recommending them, because vendor VRAM numbers rarely match what actually loads.

What actually fits in 48GB unified memory

Model	Params	Memory at Q4	License	Fit
Llama 3.3 70B	70B	~48 GB	Llama 3.3 Community	Right at the ceiling, tight
Ornith 1.0 35B	35B	~20 GB	MIT	Comfortable, room for long context
Gemma 3 27B	27B	~15 GB	Gemma Terms of Use	Comfortable
OpenAI gpt-oss-20b	20B total / 3.6B active (MoE)	~12 GB	Apache 2.0	Comfortable

70B at Q4 leaves almost no headroom on a 48GB machine

Llama 3.3 70B's Q4_K_M build needs roughly the full 48GB budget once you account for the KV cache, so close every other app before loading it. If that feels too tight for daily use, Ornith 1.0 35B is the practical alternative on the same hardware, with real room to spare.

Which quantization to pick

MLX is the format built for Apple's unified memory architecture and is generally the faster choice on an M-series chip, since it skips the cross-platform layer GGUF uses to run everywhere. GGUF through Ollama remains the simpler on-ramp and is what makes the install command below a single line. At the 70B size specifically, either format needs the same roughly 48GB, the difference between them is speed and ease of setup, not what fits. See the GGUF vs MLX vs NVFP4 explainer for the full comparison.

Install and run

zsh - Ornith 1.0 35B, the faster daily driver

Comfortable fit at ~20GB, leaves the rest of 48GB free for context and other apps

$ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF

>>> Send a message

zsh - Llama 3.3 70B, right at the ceiling

Close other apps first; this uses nearly the full 48GB unified pool

$ollama pull llama3.3:70b

pulling model weights... ████████████ 100% 43 GB

success

$ollama run llama3.3:70b

>>> Send a message

Real-world speed: what's measured and what isn't

The llama.cpp project's own published Apple Silicon Metal benchmarks measured the M4 Pro's 20-core GPU configuration, the same GPU config in the 48GB Mac mini, generating a 7B Llama model at 50.7 tokens per second at Q4_0. That number is a real, verified baseline for how fast this chip's GPU actually is; it is not a 70B number, and generation speed drops substantially as a model's memory footprint approaches the chip's full unified-memory pool, since decoding becomes bound by memory bandwidth (273 GB/s on the M4 Pro) rather than compute.

For a data point on a large mixture-of-experts model, Apple's own mlx-lm benchmark suite measured Qwen3-30B-A3B, a 30B-parameter MoE model with a small active-parameter count similar in spirit to gpt-oss-20b, generating at 113 tokens per second at 4-bit on an M4 Max, a faster sibling chip with more GPU cores and higher bandwidth than the M4 Pro. That is not a Mac mini M4 Pro number either; it shows the ceiling MoE architectures can reach on Apple Silicon when the active parameter count stays low, dense 70B models like Llama 3.3 do not get that shortcut.

We could not find a solidly documented 70B number for this exact chip

Public benchmarks for Llama 3.3 70B specifically on a 48GB M4 Pro Mac mini are thin and inconsistent across the sources we checked. If you need an exact figure before buying, the honest answer is to run it yourself: llama-bench (bundled with llama.cpp) or mlx_lm.generate --verbose both print tokens-per-second directly, and take five minutes to run once the model is downloaded.

Where to go from here

Want the same model class on a PC GPU instead? See the 24GB VRAM setup guide.
Coming from the entry-level Mac? The Mac M4 16GB setup guide covers the base configuration.
New to quantization formats? Read the GGUF vs MLX vs NVFP4 explainer first.

Local models run better with more VRAM. CompareRTX GPUs on Amazonbefore you upgrade.(affiliate link. We may earn a commission at no extra cost. Disclosure)

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