Updated 2 November 2026 · 31 models · 80 machines

What AI can your machine run?

Pick your Mac or PC and see which free AI models run on it, how fast, and the exact command to start. No signup.

Not sure? On a Mac: Apple menu, About This Mac. On Windows: Task Manager, Performance, GPU.

16/ 31

Your M4 16GB

runs 16 of 31 popular local AI models well, and 2 more slowly.

Best pick for chat: Gemma 4 12B. For coding: Qwen2.5 Coder 7B.

Chat · Google

Gemma 4 E4B

Runs great

Best small model for everyday questions and writing.

20 words/ssmooth
Needs ~7.4 GB (6.6 GB download)
Open weights
Reasoning · DeepSeek

DeepSeek R1 8B

Runs great

Thinks step by step before answering. Good for maths and logic.

12 words/ssmooth
Needs ~6 GB (5.2 GB download)
MIT
Coding · Alibaba

Qwen2.5 Coder 7B

Runs great

Light code helper for autocomplete on smaller machines.

14 words/ssmooth
Needs ~5.4 GB (4.7 GB download)
Apache 2.0
Chat · Google

Gemma 4 E2B

Runs great

Tiny and quick. Runs on almost anything, phones included.

32 words/sfaster than you can read
Needs ~5.3 GB (4.6 GB download)
Open weights
Chat · Alibaba

Qwen 3.5 4B

Runs great

Small all-rounder that reads images too.

19 words/ssmooth
Needs ~4 GB (3.3 GB download)
Apache 2.0
Voice and music · ACE Studio

ACE-Step 1.5

Runs well

Makes full songs with vocals from lyrics.

about 3 minper 1 minute song
Needs ~12 GB

Use the ACE-Step app

Apache 2.0
Images · Black Forest Labs

FLUX.2 klein 4B

Runs well

Sharp, modern images and photo editing from a small model.

about 34 sper image
Needs ~10 GB

Use Draw Things (Mac) or ComfyUI

Apache 2.0
Reasoning · Microsoft

Phi-4 14B

Runs well

Strong at maths and reasoning for its size.

7 words/sabout reading speed
Needs ~10.1 GB (9.1 GB download)
MIT
Chat · Google

Gemma 4 12B

Runs well

Sweet spot for 16 GB machines. Good writing and reasoning.

8 words/sabout reading speed
Needs ~8.9 GB (8 GB download)
Open weights
Images · Stability AI

Stable Diffusion XL

Runs well

Classic image model with thousands of community styles.

about 18 sper image
Needs ~8 GB

Use Draw Things (Mac) or ComfyUI

OpenRAIL++
Voice and music · Alibaba

Qwen3 TTS 1.7B

Runs well

Clone a voice from a short sample. Very natural.

about 52 sper minute of speech
Needs ~8 GB

Use Voicebox, or the Qwen3-TTS repo

Apache 2.0
Chat · Alibaba

Qwen 3.5 9B

Runs well

Strong everyday helper, 256K context, sees images.

10 words/sabout reading speed
Needs ~7.4 GB (6.6 GB download)
Apache 2.0
Vision · Alibaba

Qwen3 VL 8B

Runs well

Reads photos, screenshots and documents.

11 words/sabout reading speed
Needs ~6.9 GB (6.1 GB download)
Apache 2.0
Voice and music · OpenAI

Whisper large-v3 turbo

Runs well

Speech to text in 90+ languages. Free, private transcripts.

about 1 minper 10 minutes of audio
Needs ~4 GB

Use MacWhisper, or whisper.cpp

MIT
Voice and music · hexgrad

Kokoro TTS

Runs well

Natural text to speech from a tiny 82M model.

about 11 sper minute of speech
Needs ~2 GB

pip install kokoro

Apache 2.0
Chat · OpenAI

gpt-oss 20B

Just fits

OpenAI's open model. Smart for its size and fast because only part of it runs per word.

13 words/ssmooth
Needs ~15.2 GB (14 GB download)
Apache 2.0
Images · Tongyi

Z-Image Turbo 6B

Runs, slowly

Great photo-real scenes in a few steps. Weak at writing text in images.

about 2 minper image
Needs ~16 GB

Use ComfyUI, or mflux on a Mac

Apache 2.0
Video · Alibaba

Wan 2.2 5B

Runs, slowly

Text or image to video on a single consumer GPU.

about 27 minper 4 second clip
Needs ~16 GB

Use ComfyUI

Apache 2.0
Chat · DeepSeek

DeepSeek V3 671B

Too big

Frontier class. Needs a 512 GB Mac Studio or a server.

Needs ~424.7 GB (404 GB download)
DeepSeek licence
Chat · Alibaba

Qwen 3.5 122B A10B

Too big

Near frontier quality at home, if you have 96 GB or more.

Needs ~85.5 GB (81 GB download)
Apache 2.0
Chat · OpenAI

gpt-oss 120B

Too big

OpenAI's big open model. Fast for its size on high-memory machines.

Needs ~68.8 GB (65 GB download)
Apache 2.0
Chat · Meta

Llama 3.3 70B

Too big

Older but solid large model. Needs serious memory.

Needs ~45.6 GB (43 GB download)
Llama licence
Video · Alibaba

Wan 2.2 14B

Too big

Best open video quality, but heavy.

Needs ~32 GB
Apache 2.0
Video · Lightricks

LTX-2.3

Too big

Short clips with real camera motion. Detail is soft at low settings.

Needs ~24 GB
Free under $10M revenue
Coding · Alibaba

Qwen 3.6 35B A3B

Too big

Fast coding and agent model, mixture of experts.

Needs ~24.6 GB (23 GB download)
Apache 2.0
Chat · Alibaba

Qwen 3.5 35B A3B

Too big

Very fast for its size. Great general assistant.

Needs ~23.6 GB (22 GB download)
Apache 2.0
Chat · Google

Gemma 4 31B

Too big

One of the best models that fits a 24 GB GPU or 32 GB Mac.

Needs ~21.5 GB (20 GB download)
Open weights
Reasoning · DeepSeek

DeepSeek R1 32B

Too big

Serious reasoning on a 24 GB GPU or 32 GB Mac.

Needs ~21.5 GB (20 GB download)
MIT
Coding · Alibaba

Qwen3 Coder 30B

Too big

Top local coding model for 24 GB GPUs and 32 GB Macs.

Needs ~20.4 GB (19 GB download)
Apache 2.0
Coding · Alibaba

Qwen 3.6 27B

Too big

Newest Qwen, tuned for agentic coding and long tasks.

Needs ~19.9 GB (18.5 GB download)
Apache 2.0
Chat · Google

Gemma 4 26B A4B

Too big

Mixture of experts: big-model quality at small-model speed.

Needs ~19.4 GB (18 GB download)
Open weights

Best value machines for local AI

Five machines that cover most budgets, with how many of the 31 models each runs well.

As an Amazon Associate I earn from qualifying purchases.

Mac mini with M4, 24 GB

Runs 20 of 31 models well. Cheapest way into useful local AI. Quiet and small, and runs 9B to 12B models at about reading speed.

See prices

Mac mini with M4 Pro, 48 GB

Runs 27 of 31 models well. The sweet spot. Fits 30B class models, and the fast mixture of experts models run at about 40 words a second.

See prices

RTX 5060 Ti 16 GB

Runs 16 of 31 models well. Best budget upgrade for an existing PC. Fast for anything up to about 14 GB.

See prices

Ryzen AI Max+ 395 mini PC, 128 GB

Runs 27 of 31 models well. Huge shared memory for the price. Runs gpt-oss 120B and Qwen 3.5 122B well. Dense 70B models load but run slowly.

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RTX 5090 32 GB

Runs 26 of 31 models well. The fastest consumer card. Best for image and video generation.

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What an upgrade would unlock

The models you would gain with more memory or a stronger chip.

As an Amazon Associate I earn from qualifying purchases.

Mac with M4, 24 GB

Unlocks 4 more: Gemma 4 26B A4B, Qwen3 Coder 30B, Z-Image Turbo 6B, Wan 2.2 5B.

See prices

Mac with M5 Pro, 48 GB

Unlocks 11 more: Gemma 4 26B A4B, Qwen 3.5 35B A3B, Gemma 4 31B, Qwen3 Coder 30B and more.

See prices

Mac with M5 Max, 128 GB

Unlocks 14 more: Gemma 4 26B A4B, Qwen 3.5 35B A3B, Gemma 4 31B, Llama 3.3 70B and more.

See prices
Too slow at home? Rent a cloud GPU by the hour and run any of these models.Try RunPod

How it works

How do you work out what runs?

Each model needs a certain amount of memory: its download size plus room for the conversation. If that fits in your graphics memory (or a Mac's unified memory), it runs fast. If it only fits with help from normal RAM, it runs slowly. Speed is estimated from your memory bandwidth, because generating text is mostly limited by how fast the chip can read the model.

What is Ollama and how do I start?

Ollama is a free app for Mac, Windows and Linux that downloads and runs these models with one command. Install it from ollama.com, open a terminal, and paste the command shown on any model card. Prefer buttons? LM Studio does the same with a normal app window.

Is running AI locally private?

Yes. Once downloaded, the model runs entirely on your computer. Your prompts, files and photos never leave the machine, and it works offline.

Mac or PC for local AI?

Macs share one big pool of memory with the GPU, so a 64 GB or 128 GB Mac can load models no gaming card can. NVIDIA cards are faster for models that fit in their memory, and best for image and video tools. For the biggest models per dollar, a high-memory Mac or a Ryzen AI Max box wins.

Why are my real speeds different?

Longer conversations, bigger context windows, other apps and different quantizations all change speed and memory. Treat the numbers here as a good first guide, not a lab measurement.

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