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Perplexity Computer Can Now Run Fully Locally on Mac

Perplexity today said it is updating Perplexity Computer to run fully locally on Mac. The local version runs on Qwen 3.6 or PPLX Pro (a post-trained version of Qwen) in the Perplexity app for Mac on supported Apple silicon Macs.

perplexity computer
Perplexity highlights the new Mac mini machines and says 36GB of memory or more is recommended to run locally. With the local setup, the AI model, task planning, reasoning, and file processing are handled on-device. Code and tool execution run in isolated sandbox environments.

Computer can use native apps on the Mac and complete tasks without sending personal data to the cloud. Perplexity says it can work across apps like Notes, Messages, Mail, Keynote, and Numbers.

Perplexity also offers Hybrid Compute, which combines frontier models that run in the cloud with local models to process information privately. It's an alternative for users who want local processing but don't have a Mac that's powerful enough. Hybrid Compute works on Macs with as little as 24GB of memory,

When running Computer locally, if a task requires current information, browser use, or cloud-connected apps, users can authorize cloud access and decide when to spend credits. There's also an option to call on one of more than 15 frontier models for tougher problems when needed.

Local Computer for Mac is available now for Perplexity Pro, Max, and Enterprise subscribers. Users can download Perplexity for Mac and then choose the fully local option to use it.

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Top Rated Comments

Seoras Avatar
4 days ago at 12:15 am

Have you found local models to do quite well for translation?
Yes, that translategemma does very well. The beauty of using the local model with a cloud model (e.g. OpenAI GPT like I do) is that one can proof read and critique the other. The problem with LLM translations is the verification.
If your prompting is solid then the hierarchy approach to using LLMs is the way to go. LLM's running LLM's.

Here's a fun fact. On my Mac Mini M4 Pro with 64GB of RAM I can run that 29GB 27b model doing translations (GPU's at 100%) and while it's working on that I can compile a large app on either Xcode or Android (or probably both at the same time!) pushing the CPU's to 100% and the machine is still responsive and the compile times not impacted too badly (while also driving two 5K displays!). That gets the, normally silent, fan blowing! It's rare to see both CPUs and GPUs flat out at the same time.
I feel like I've yet to really reach the limits of what this 2 year old machine is capable of doing in terms of work load.
Score: 9 Votes (Like | Disagree)
4 days ago at 11:22 pm
perplexity will go bankrupt soon lol

but local ai is very much the future
Score: 5 Votes (Like | Disagree)
4 days ago at 10:01 pm
Good to know about this. Mac mini or any Mac with the right specification can be a great device to run AI locally. They are incredible computers.
Score: 3 Votes (Like | Disagree)
baryon Avatar
3 days ago at 05:30 am
Why do you have to pay a subscription fee to run a free open source model on your own hardware?

Just use oMLX, it's free and can run the same model.
Score: 2 Votes (Like | Disagree)
Seoras Avatar
4 days ago at 10:21 pm
Only 2 comments so far?

After forking out a ton of cash for a Mac with memory north of 32G you still need to pony up $20 a month to run it locally, that might explain the silence in the room here. Not many will have that amount of memory.

I'm paying OpenAI the same for "Plus" and using ChatGPT (previously known as Codex) Mac app that does pretty much all I need right now, including using a local LLM (translategemma:27b-it-q8_0 29GB) via Ollama to do more specialised stuff (language translation) without burning through my usage allotment too quickly.

I can see a niche for Perplexity in corporate requirements of localised, private and secure work. As an independent developer it doesnt suit my needs. It can't be a large demograph to target? Anyone using this?
Score: 2 Votes (Like | Disagree)
v0lume4 Avatar
3 days ago at 09:42 pm

Yes, that translategemma does very well. The beauty of using the local model with a cloud model (e.g. OpenAI GPT like I do) is that one can proof read and critique the other. The problem with LLM translations is the verification.
If your prompting is solid then the hierarchy approach to using LLMs is the way to go. LLM's running LLM's.

Here's a fun fact. On my Mac Mini M4 Pro with 64GB of RAM I can run that 29GB 27b model doing translations (GPU's at 100%) and while it's working on that I can compile a large app on either Xcode or Android (or probably both at the same time!) pushing the CPU's to 100% and the machine is still responsive and the compile times not impacted too badly (while also driving two 5K displays!). That gets the, normally silent, fan blowing! It's rare to see both CPUs and GPUs flat out at the same time.
I feel like I've yet to really reach the limits of what this 2 year old machine is capable of doing in terms of work load.
What a cool setup. That little Mac mini is a beast! My base M4 mini would keep me waiting for some time compared to yours. 😛

I didn't know there were purpose built local models for translation like TranslateGemma. How cool.
Score: 1 Votes (Like | Disagree)
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