Best local LLMs for MacBook Pro M5 Max 128GB
With Apple M5 Max, 128GB the strongest open models that run well are gpt-oss-120b, Gemma 4 E4B. Speeds are estimates based on memory bandwidth.
Your machine
Chat and writing
- 1gpt-oss-120bOpenAI · 117B · Apache 2.0~101 tok/sFastQ5 · 83 GB download · Mixture of experts: 117B total, 5.1B active per token, so it runs fast for its size. · Official page
- 2Mistral Small 4Mistral · 119B · Apache 2.0~80 tok/sFastQ5 · 85 GB download · Mixture of experts: 119B total, 6.5B active (Mistral). · Official page
- 3Ornith 1.5 122BOrnith AI · 122B · MIT~52 tok/sFastQ5 · 87 GB download · Mixture of experts built on Alibaba's Qwen3.5 122B-A10B architecture, so about 10B active per token (Ornith's card doesn't state the active count), 262K context. Weights posted Oct 16, 2026. Ornith says it serves on a single 80 GB GPU at full precision; 4-bit builds need far less. · Official page
- 4Qwen3.8 27BQwen · 27.8B · Apache 2.0~12 tok/sUsableQ8 · 30 GB download · Dense 27B language model with a built-in vision encoder (27.8B weights in total). 262K context natively. · Official page
Coding
- 1gpt-oss-120bOpenAI · 117B · Apache 2.0~101 tok/sFastQ5 · 83 GB download · Mixture of experts: 117B total, 5.1B active per token, so it runs fast for its size. · Official page
- 2Mistral Small 4Mistral · 119B · Apache 2.0~80 tok/sFastQ5 · 85 GB download · Mixture of experts: 119B total, 6.5B active (Mistral). · Official page
- 3Ornith 1.5 122BOrnith AI · 122B · MIT~52 tok/sFastQ5 · 87 GB download · Mixture of experts built on Alibaba's Qwen3.5 122B-A10B architecture, so about 10B active per token (Ornith's card doesn't state the active count), 262K context. Weights posted Oct 16, 2026. Ornith says it serves on a single 80 GB GPU at full precision; 4-bit builds need far less. · Official page
- 4Qwen3.8 27BQwen · 27.8B · Apache 2.0~12 tok/sUsableQ8 · 30 GB download · Dense 27B language model with a built-in vision encoder (27.8B weights in total). 262K context natively. · Official page
Small and fast
- 1Gemma 4 E4BGoogle · 8B · Apache 2.0~77 tok/sFastQ8 · 8 GB download · Mixture of experts: 8B total, 4.5B active per token, so it runs fast for its size. · Official page
- 2Ministral 3 8BMistral · 8B · Apache 2.0~43 tok/sFastQ8 · 8 GB download · Official page
- 3Qwen3 8BQwen · 8.2B · Apache 2.0~42 tok/sFastQ8 · 9 GB download · Official page
- 4Qwen3 4BQwen · 4B · Apache 2.0~87 tok/sFastQ8 · 4 GB download · Official page
Quick facts
How much memory can a MacBook Pro M5 Max 128GB use for AI models?
About 96 GB of its 128 GB, after leaving room for the system. That is our estimate of what is usable for model weights.
What is the largest model a MacBook Pro M5 Max 128GB can run?
In our list, Ornith 1.5 122B at Q5 (about 87 GB) fits. Big models may run slowly, so check the speed estimate.
What is the fastest good model on a MacBook Pro M5 Max 128GB?
gpt-oss-120b at Q5 runs at an estimated 101 tokens per second, based on memory bandwidth.
On a 32GB Mac? See the best local LLM for a 32GB Mac.
Other setups: Mac mini M4 Pro 64GB MacBook Air M4 16GB MacBook Pro M4 Max 128GB RTX 4090 (24GB) RTX 3060 12GB RTX 5090 (32GB) MacBook Pro M4 Pro 24GB MacBook Pro M4 Pro 48GB Mac Studio M4 Max 64GB RTX 4070 (12GB) RTX 4080 (16GB) PC with 32GB RAM (no GPU) 8GB VRAM 16GB VRAM 16GB RAM PC MacBook Air M5 24GB MacBook Pro M5 Pro 48GB
How the estimates work
Fit: a model fits if its download size plus about 1.5 GB for context fits in usable memory. On a Mac that is about two thirds of RAM (three quarters from 32GB up), since macOS keeps some for itself. On a PC it is your GPU's VRAM minus 1 GB, or 60% of RAM when running on the CPU only.
Quant: we pick the highest quality that fits: Q8 (about 8.5 bits per weight), then Q5 (5.7), then Q4 (4.8). Q4 and Q5 keep most of the quality at a fraction of the size.
Speed (estimate): generating text is limited by memory bandwidth, because every token reads the active weights once. We use bandwidth x 0.6 / bytes per token. Mixture-of-experts models only read their active experts, so they run much faster than their total size suggests. Real speed varies with the app, context length and settings, so treat it as a rough guide.
How to run a local model
Ollama: install from ollama.com, then find the model in the Ollama library and run it with one command. LM Studio: install from lmstudio.ai, search the model name, and pick the quant shown here. On a Mac, LM Studio's MLX builds are usually fastest.
FAQ
What is the best local LLM for a Mac?
It depends on RAM. With 16GB, small models like Gemma 4 E4B or Qwen3 8B run well. With 64GB, mixture-of-experts models like gpt-oss-120b don't fit, but Gemma 4 31B and Qwen3.6 27B do, and 128GB opens up gpt-oss-120b and Mistral Small 4. Pick your Mac above for the exact list.
How much RAM do I need to run a local AI model?
About 0.6 GB per billion parameters at Q4, plus a little for context. An 8B model needs about 6 GB, a 30B model about 20 GB, and a 70B model about 44 GB.
Can I detect my specs automatically?
Partly. Browsers share your platform, CPU core count, a rough RAM hint and sometimes the GPU maker. They hide the exact chip and memory size, so the finder makes a best guess and you confirm it with a tap.
Is a local model cheaper than an API?
If you already own the hardware, local models cost only electricity. For top quality, hosted models still lead; compare them on our AI API prices page.