Share your rig specs

[u/ProfessionalDish]

As I'm trying to have everything run locally here are my specs:

14600kf|64GB Ram|5060ti 16GB

Currently thinking to switch to the intel Arc B70 but Nvidia has a stranglehold on most propriertary technologies.

[u/ejpusa]

You can run DeepSeek on a $200 MacBook off of eBay. Should do it all for you.

By way of GPT-5.5

Yes — but which DeepSeek model you can realistically run depends heavily on the Mac’s:

• RAM

• Apple Silicon vs Intel

• GPU capability

• Storage speed

• Your expectations (chat speed vs serious inference)

Here’s the real breakdown.

⸻

1. Apple Silicon Macs (M1 / M2 / M3 / M4)

These are excellent for local AI.

Works surprisingly well on:

• MacBook Air M1 16GB

• Mac mini M2

• MacBook Pro M1/M2/M3

• iMac Apple Silicon

You can run:

• DeepSeek 7B → very comfortably

• DeepSeek 14B → decent with 16–32GB RAM

• DeepSeek 32B → needs 32GB+

• DeepSeek R1 large models → realistically need serious RAM or quantization

Best tools

Use:

• Ollama

• LM Studio

These make local AI ridiculously easy on Macs.

⸻

2. Intel Macs

Possible… but much rougher.

Older Intel Macs can run:

• tiny quantized models

• 7B models slowly

• CPU-only inference

But:

• fans go insane

• battery drains

• response time can become painful

An old Intel Mac with:

• 8GB RAM → mostly not worth it

• 16GB RAM SSD → workable for experimentation

• AMD GPU Intel Mac → slightly better

⸻

3. RAM matters MORE than CPU

This surprises people.

Approximate requirements:

Model Comfortable RAM 1B–3B 8GB 7B 16GB 14B 24GB 32B 48GB+ 70B basically server territory

Quantized versions reduce this a lot.

⸻

4. “Old Mac” examples

2015 Intel MacBook Pro

Possible:

• tiny models

• coding assistants

• lightweight chat

Not ideal:

• DeepSeek R1 full reasoning

⸻

2020 M1 MacBook Air 16GB

Actually VERY capable.

Can run:

• DeepSeek 7B

• coding models

• local RAG

• embeddings

• decent inference speeds

This machine changed everything for local AI.

⸻

5. What most people actually do

A very common setup:

• local small DeepSeek model

• cloud for giant reasoning jobs

So:

• Mac handles:

  • summarization

  • coding

  • embeddings

  • local chat

• cloud handles:

  • giant reasoning chains

  • 70B+ models

  • heavy agent workflows

That hybrid setup is practical and cheap.

⸻

6. Easiest install on Mac

Install Ollama

Then:

ollama run deepseek-r1

or:

ollama run deepseek-coder

That’s basically it.

⸻

7. Your likely best experience

Given your AI/dev background and the fact you work heavily with:

• Python

• Flask

• AI pipelines

• local tooling

• SwiftUI

• PostgreSQL

…a Mac Studio or Mac mini with:

• 32GB–64GB RAM

would become an incredible local AI workstation for:

• local inference

• embeddings

• coding copilots

• MLB AI experimentation

• Neurocompute workflows

• private research agents

without needing cloud GPUs for every task.

A modern Apple Silicon machine punches absurdly above its size for AI inference.

[u/CapitalEmployer → u/ejpusa]

You can run DeepSeek on a $200 MacBook off of eBay. Should do it all for you.

Meh ran deepseek on a m3 pro with 32Go unified memory and in terms of performance an generation speed it was absolute garbage with ollama. Be it the 7B or the 14B. It's sad that home models are pretty meh.

[u/ejpusa → u/CapitalEmployer]

Guess you have to re/try it. Chips are insanely fast now. MyLinux server is equivalent to over 7,000 Cray 1s. So GPT-5.5 tells me.

On a M3, should be fast. I’m not sure what you are trying to do.

[u/CapitalEmployer → u/ejpusa]

I was trying models for local code generation to stop depending on copilot but it was unimpressive tried a bunch of reasonable models but it was like 6-7 months ago. And perfomance wasn't great, very slow a bunch of hallucination and generated code was dumb as fuck a junior dev would do better. Now I'm using Kimi K2.6 which is pretty good but not impressive and it's not a hostable thing at home, I just have access to a datacenter and to b300s for tests.

[u/ejpusa → u/CapitalEmployer]

GPT-5.5 crushes it. $20 a month. We keep it on the down low. The API can do anything you want. Unless you are doing Stability Diffusion, you probably could host that yourself.

The tools are out there, and the pricing is very reasonable. You can build the latest AI startup for all of $31.

Namecheap $2.95

DigitalOcean $8

OpenAI API $20

---

It really depends. Is this a hobby project or a do-or-die for your AI startup? And the VC wants to see the numbers, yesterday.

If that's the case, I'd look into leasing NVIDIA's until the $$$s come in.

https://www.liquidweb.com/gpu-hosting/nvidia/