How much RAM do I need to run a local LLM on a Mac?

16 GB runs small models in the 7B to 12B range at usable speed. 32 to 36 GB is the practical floor for a mid-size model you would rely on daily. 64 GB covers 30B class models comfortably with room for context, and 128 GB is what you need for 70B class weights. Unified memory cannot be upgraded after purchase, so buy more than you think you need.

Can local models replace ChatGPT or Claude in 2026?

Not for hard reasoning, long agentic chains, or frontier code work. Local open-weight models are competitive for summarization, extraction, classification, rewriting, and private drafting. The realistic 2026 setup is hybrid: local for private and high-volume tasks, frontier cloud models for the work that decides something.

What is the easiest way to run a local model on macOS?

Ollama for command line and API use, LM Studio if you want a graphical interface and a model browser, and MLX directly when you want maximum performance or plan to fine-tune. All three are free and install in minutes on Apple silicon.

Does running AI locally actually protect privacy?

Yes, when the model runs on your machine, the prompt and the output never leave it, which removes third-party processing from the picture entirely. That matters for regulated data, client material under NDA, and anything you would not paste into a hosted chat window. Local execution does not by itself satisfy legal obligations, but it removes the largest one.

Local AIApple SiliconPrivacy

Local AI on a Mac in 2026: What Runs Well, What Does Not, and When to Stay in the Cloud

Q: Is MLX faster than llama.cpp on Apple silicon?

MLX is generally faster on Apple silicon because it is built for the unified memory architecture, with community benchmarks reporting meaningful gains over llama.cpp on recent M-series chips. The gap varies by model, quantization, and context length, so treat any single number as indicative rather than exact.

A Mac with enough unified memory is a genuinely good inference box in 2026. It is still not a frontier model, and pretending otherwise wastes a good machine.

Eliran Suisa

August 27, 2026

8 min read

TL;DR

Memory bandwidth and total unified memory decide local performance, not GPU core count. Buy memory at purchase time, it cannot be added later.
Stack: Ollama for daily use and APIs, LM Studio for a GUI, MLX directly for maximum speed and fine-tuning.
Local wins on private, repetitive, and offline work. Cloud still wins on hard reasoning and long agent chains.
The right 2026 setup is hybrid. Route by sensitivity and difficulty, not by ideology.

Memory Bandwidth Is the Bottleneck

Local inference speed on Apple silicon is governed by memory bandwidth and how much of the model fits in unified memory, not by GPU cores. Token generation reads the model weights on every token, so the number that predicts your tokens per second is gigabytes per second of memory bandwidth, which on current M-series parts ranges from a few hundred on the Pro tier to roughly double that on Max configurations.

Two consequences follow. A model that spills out of unified memory falls off a cliff rather than degrading gracefully, so fitting the weights matters more than any tuning flag. And unified memory is soldered, so the memory decision you make at checkout is the ceiling for the life of the machine. If you are buying a Mac partly for local AI, memory is the line item that deserves the money.

The Stack: Ollama, LM Studio, MLX

Three tools cover every reasonable use case, and they layer rather than compete. Ollama is the default for everyday chat, scripting, and serving a local API to other apps, and recent versions use MLX under the hood on Apple silicon. LM Studio is the graphical option, with a model browser, quantization picker, and an OpenAI-compatible server toggle. MLX and MLX-LM are the direct route when you want maximum throughput or plan to fine-tune with LoRA on your own data.

MLX is the reason local inference on Macs got good. It targets the unified memory architecture directly, and community benchmarks through 2026 consistently report it ahead of llama.cpp on recent Apple silicon, with the margin varying by model, quantization, and context length. Start with Ollama, move to MLX when you have a reason.

What to run, by memory tier

16 GB

7B to 12B class models at 4-bit. Fine for summarization, tagging, and quick rewrites. Keep context short.

32 to 36 GB

Mid-size mixture-of-experts models. The first tier where a local model can be a real daily driver.

64 GB

30B class weights with generous context, including coder-specialized models that are genuinely useful.

128 GB

70B class at 4-bit, interactive speeds, and room to keep a second model resident for comparison.

What Local Models Are Actually Good At

Open-weight models running locally are strong on bounded, repetitive language work: summarizing, extracting fields into JSON, classifying, translating, rewriting tone, and drafting from a template. These are the tasks where a good 30B model at 4-bit is indistinguishable from a hosted mid-tier model for practical purposes, and where volume makes per-token pricing annoying.

Coder-tuned models in the 30B class are the standout category of the last year. They complete functions, explain unfamiliar code, and write tests at a quality that would have required a hosted frontier model in 2024. They are not going to architect your system, and they lose the long-horizon agentic work outright.

What They Are Not Good At

Local models lose on hard reasoning, long context, and multi-step agent runs, and the gap is not close. A frontier hosted model with extended thinking will solve problems a local 70B simply cannot, and the difference compounds when a task requires holding many constraints at once across a long context window. Million-token context is table stakes for flagship hosted models in 2026 and out of reach locally.

They also lose on tool ecosystems. Hosted providers ship web access, code execution, file handling, and connectors that would each be a weekend project locally. If your workflow leans on those, local is a component, not a replacement.

The Hybrid Rule

Route by sensitivity and difficulty: private or high-volume work goes local, hard or decisive work goes to a frontier model. In practice that means client documents under NDA, personal financial material, unreleased code, and any bulk pipeline run locally, while architecture decisions, ambiguous analysis, and anything I would show a client get sent to two or three hosted models in parallel so I can compare them.

The comparison habit matters more than the model choice. When a local model and a hosted model disagree about something factual, that disagreement is the useful signal, and it is the same argument as running four LLMs in parallel on the same job. I keep the hosted providers in one native macOS window through ChatAxis and a local endpoint next to them, which makes the comparison a keystroke instead of a project.

The Privacy Case Is the Strongest One

A model running on your machine cannot leak a prompt to a third party, because the prompt never leaves the machine. That is a categorical difference rather than a policy promise, and it is why local inference belongs in any workflow touching regulated data, client material, or unreleased work.

It is also why local matters more in 2026 than it did last year. Disclosure and record-keeping duties are tightening, including the EU AI Act transparency rules that became applicable in August 2026. Keeping a class of work entirely off third-party infrastructure narrows the surface you have to reason about.

The Cost Math

Local inference is free per token and expensive up front. A memory-heavy Mac is a four-figure decision, and the payback depends entirely on volume: at a few hundred prompts a month, hosted subscriptions win easily. At tens of thousands of bulk classification calls a month, local wins within a year and keeps winning.

The honest framing is that most people buy the Mac anyway. If the machine is already on your desk, the marginal cost of running a local model on it is electricity and disk space, which makes the question not whether it is worth buying hardware, but which slice of your work should stop going to an API. For how I split the rest of the stack across providers, see building a stack that survives model churn.

FAQ

How much RAM do I need for local AI on a Mac?

16 GB runs 7B to 12B models, 32 to 36 GB is the practical daily driver floor, 64 GB handles 30B class comfortably, and 128 GB is required for 70B class weights. Memory is not upgradeable after purchase.

Is MLX faster than llama.cpp on Apple silicon?

Generally yes, because MLX targets the unified memory architecture directly. Reported margins vary by model, quantization, and context length, so treat published numbers as indicative.

Can a local model replace ChatGPT or Claude?

Not for hard reasoning, long context, or agentic chains. It can replace them for summarization, extraction, classification, and private drafting, which is a large share of daily volume.

What is the easiest way to start?

Install Ollama, pull a mid-size 4-bit model that fits your memory, and point your existing tools at the local endpoint. Switch to LM Studio if you prefer a GUI, or MLX if you need speed.

Does local AI satisfy privacy or compliance requirements?

It removes third-party processing, which is usually the biggest exposure, but it is not automatic compliance. You still owe disclosure, record-keeping, and access controls for the systems you deploy.