Can the M4 Max run Llama 3 70B?

Yes. The M4 Max with 64GB unified memory runs Llama 3 70B at Q4-Q6 quantization comfortably. The 128GB configuration can run it at Q8 or even FP16. Expect 8-12 tokens per second at Q4, which is usable for interactive chat.

Is the M4 Max good for AI?

The M4 Max is excellent for model capacity thanks to unified memory. With 64GB or 128GB, it can load models that no consumer NVIDIA GPU can fit. The trade-off is speed — expect 2-3x slower inference compared to an RTX 4090 for the same model.

Should I get the 64GB or 128GB M4 Max for AI?

If you plan to run 70B+ models at high quantization, image generation with Flux at FP16, or video generation with large models, 128GB is worth the upgrade. 64GB handles most 70B models at Q4 and all models under 30B at high quality.

What is better for AI, M4 Max or RTX 4090?

The RTX 4090 is faster per-token and per-image. The M4 Max can load larger models thanks to unified memory. Choose NVIDIA for speed on models that fit in 24GB. Choose the M4 Max for running models that exceed 24GB without needing a multi-GPU or professional setup.

March 26, 2026apple-silicon, m4-max, mac, hardware

M4 Max for AI — Running Local Models on Apple Silicon

Complete guide to running AI models locally on the Apple M4 Max. Covers unified memory advantages, LLM performance, image and video generation, MLX vs llama.cpp, and how the M4 Max compares to NVIDIA GPUs and the M4 Pro.

The Apple M4 Max is one of the most interesting chips for local AI — not because it is the fastest, but because unified memory lets it load models that no consumer GPU can touch. With 64GB or 128GB of shared memory, the M4 Max blurs the line between consumer and professional AI hardware. This guide covers what it can run, how fast, and when it makes sense over an NVIDIA GPU.

M4 Max Specs for AI

Spec	M4 Max (64GB)	M4 Max (128GB)
Unified Memory	64 GB	128 GB
Memory Bandwidth	~546 GB/s	~546 GB/s
GPU Cores	40	40
Neural Engine	16-core	16-core
FP16 Compute	~54 TFLOPS	~54 TFLOPS

The critical number is memory bandwidth: 546 GB/s. This is slower than the RTX 4090 (1,008 GB/s) and much slower than the RTX 5090 (~1.8 TB/s). Since LLM inference is memory-bandwidth-bound, this directly affects tokens per second. But unified memory means the full 64GB or 128GB is available to the GPU — no separate VRAM limit.

The Unified Memory Advantage

On a discrete GPU, VRAM is separate from system RAM. An RTX 4090 has 24GB of VRAM, period. If a model needs 30GB, it does not fit without offloading to slower system RAM.

On the M4 Max, the GPU and CPU share the same memory pool. A 64GB M4 Max can allocate most of that memory to a model. This means:

64GB M4 Max can load models requiring up to roughly 55-58GB (leaving room for the OS and other processes)
128GB M4 Max can load models requiring up to roughly 115-120GB

No consumer NVIDIA GPU comes close to these capacities. The RTX 5090 tops out at 32GB. Matching the M4 Max 128GB would require a $6,800+ RTX Pro 6000 or a dual-GPU setup.

LLMs on the M4 Max

The M4 Max is a strong LLM machine, especially at the 128GB configuration:

64GB Configuration:

Llama 3 70B at Q4-Q6 — Fits comfortably. Q4 uses around 38GB, Q6 around 48GB. Expect 8-12 tok/s at Q4.
DeepSeek R1 at Q4 — The quantized reasoning model fits within the 64GB budget.
Qwen 3 30B at Q8 — Runs with excellent quality and generous headroom.
Mixtral 8x22B at Q4 — The large MoE model fits where most consumer GPUs cannot load it.

128GB Configuration:

Llama 3 70B at Q8 or FP16 — FP16 requires around 140GB, which is tight but possible with careful memory management. Q8 at around 70GB is very comfortable.
Llama 3 405B at Q2 — The largest open-weight model at aggressive quantization. Around 100-110GB. Functional but quality is degraded at Q2.
DeepSeek R1 at Q4-Q6 — Higher quantization levels become available.
Any model under 70B at FP16 — No quantization needed for most models.

Performance expectation: LLM inference on the M4 Max is roughly 2-3x slower than an RTX 4090 for the same model at the same quantization. An RTX 4090 generates 20-30 tok/s for Llama 3 70B Q4; the M4 Max generates 8-12 tok/s. This is still perfectly usable for interactive chat — just not as snappy.

Image Generation on the M4 Max

Unified memory removes the VRAM constraint that limits image generation on discrete GPUs:

Flux Dev at FP16 — The full 33GB model loads natively on the 64GB configuration. No FP8, no GGUF quantization needed. This is something no consumer NVIDIA GPU can do.
SDXL 1.0 and SD 3.5 — Both fit at FP16 with room for extensive ControlNet and LoRA stacks.
Qwen Image 20B — Fits at FP16 (around 42GB) on the 64GB configuration.

The speed trade-off: Image generation on the M4 Max takes roughly 2-3x longer than on an RTX 4090. A Flux image that takes 15 seconds on a 4090 might take 35-45 seconds on the M4 Max. For professional batch work this matters. For personal use and experimentation, it is perfectly acceptable.

Video Generation on the M4 Max

Video generation benefits enormously from large memory pools:

Wan Video 14B — Fits entirely in unified memory on both configurations. No offloading needed.
HunyuanVideo — The large video model loads without the aggressive offloading required on 24GB GPUs.
LTX Video 13B at FP16 — Comfortable fit on the 64GB configuration.
FramePack, CogVideoX, AnimateDiff — All run with massive headroom.

Video generation is the slowest modality on Apple Silicon due to the compute gap. Expect generation times roughly 3-4x longer than an RTX 4090. For short clips and experimentation this works. For production video pipelines, NVIDIA remains the better choice.

MLX vs llama.cpp vs Diffusers on Mac

Three main frameworks for running AI on Apple Silicon:

MLX — Apple's own framework, optimized for Apple Silicon. Best performance for supported models. Growing rapidly but does not yet cover all architectures. Use MLX when your model is supported.

llama.cpp — The universal LLM inference engine. Excellent Metal support on Mac. Slightly slower than MLX for some models but supports virtually every GGUF model. The safe default choice for LLMs.

Diffusers (with MPS backend) — Hugging Face's library works on Mac via the Metal Performance Shaders backend. Supports Flux, SDXL, SD 3.5, and most image/video models. Performance is improving but still behind CUDA.

Recommendation: Use MLX for LLMs when available, llama.cpp as the fallback. Use Diffusers with MPS for image and video generation. ComfyUI also runs on Mac with MPS support for more complex image workflows.

M4 Max vs M4 Pro: The Budget Option

Spec	M4 Pro (24GB)	M4 Max (64GB)
Unified Memory	24 GB	64 GB
Bandwidth	~273 GB/s	~546 GB/s
Best LLM	Qwen 3 30B Q4	Llama 3 70B Q6
Flux	GGUF Q8	FP16 native
Video	Wan 1.3B	Wan 14B

The M4 Pro at 24GB matches the RTX 4090 in capacity but with half the bandwidth of the M4 Max and a quarter of the RTX 4090's bandwidth. It runs smaller models well but loses the unified memory advantage that makes the M4 Max compelling. If you are buying a Mac primarily for AI, the M4 Max is the minimum worth considering.

Getting Started

Check exactly what runs on your M4 Max configuration at /macs/m4-max-64gb. Use the hardware calculator to test specific models against your setup. Browse the model catalog to find compatible models sorted by VRAM requirements.