text model · Mistral · macOS

Can I run Mixtral 8x7B on Apple M5 Pro (48GB)?

Compatibility verdict VRAM threshold engine

Yes, but tight GPU accelerated

Yes. Mixtral 8x7B runs on Apple M5 Pro (48GB) at Q4_K_M (~28.9 GB of ~32 GB usable).

Needs ~28.9 GB Device usable ~32 GB

Fits at Q4_K_M (~28.9 GB of ~32 GB usable) but with little headroom. Close other apps, or drop to a 2k context to free about a gigabyte.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 Pro (48GB) leaves ~3.1 GB of headroom.

Q4_K_M needed: ~28.9 GB
Usable on device: ~32 GB
Device memory: 48 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~32 GB usable

Q2_K

~22 GB

Q3_K_M

~25.2 GB

Q4_K_M

~28.9 GB

Q5_K_M

~35.7 GB

Q6_K

~40.7 GB

Q8_0

~48.6 GB

FP16

~95.8 GB

The line marks Apple M5 Pro (48GB)'s ~32 GB budget; rungs past it are too large.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run mixtral:8x7b

llama.cpp

$ llama-cli -hf MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1-GGUF:Q4_K_M

LM Studio

$ lms get MaziyarPanahi/Mixtral-8x7B-Instruct-v0.1-GGUF

Model Mistral

Parameters: 46.7B (MoE, 12.9B active)
Q4_K_M size: 26.49 GB
Q8_0 size: 46.22 GB
Context: 32k
Ollama tag: mixtral:8x7b

Full Mixtral 8x7B requirements →

Device macOS

Memory: 48 GB unified
Usable for weights: ~32 GB
Best runtime: MLX direct / Ollama (MLX backend)

Best models for Apple M5 Pro (48GB) →

You could also run

Run Mixtral 8x7B on other hardware

Nvidia GeForce RTX 5090 (32GB)Apple M4 Pro (48GB)Apple M4 Max (64GB)Apple M4 Max (128GB)AMD Ryzen AI Halo (128GB)Apple M5 Max (128GB)

FAQ

Can Apple M5 Pro (48GB) run Mixtral 8x7B?

Yes. Mixtral 8x7B runs on Apple M5 Pro (48GB) at Q4_K_M (~28.9 GB of ~32 GB usable).

How much memory does Mixtral 8x7B need?

It is a tight fit on Apple M5 Pro (48GB). At Q4_K_M the weights are ~26.49 GB; with KV cache and runtime overhead, budget ~28.9 GB at a 4k context. It is a Mixture-of-Experts model (46.7B total / 12.9B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Mixtral 8x7B on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Markdown

[![Mixtral 8x7B on Apple M5 Pro (48GB)](https://localmodel.run/badge/mixtral-8x7b/apple-m5-pro-48gb.svg)](https://localmodel.run/can-i-run/mixtral-8x7b/apple-m5-pro-48gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.