Best Mac for Local AI (2026): Apple Silicon Buying Guide

Published on April 10, 2026 • 18 min read

Quick answer — the best Mac for local AI in 2026 is the Mac Mini M4 Pro with 48GB ($1,799). It runs every model up to 33B at full Q4_K_M quality, handles 70B at reduced quality, and undercuts an equivalent NVIDIA PC build. Buying on a budget? The base Mac Mini M4 16GB ($799) runs 7B–13B fast. Need 70B at full quality and real speed? The Mac Studio M4 Max 64GB ($3,499) hits ~12.5 tok/s on Llama 3.1 70B at 546 GB/s. And if you want to run frontier-size models like the full DeepSeek-R1 671B entirely in memory, only the Mac Studio M3 Ultra (96–256GB, from $3,999) can do it — its 800 GB/s bandwidth and giant memory pool are unmatched on any single consumer machine.

Updated June 20, 2026: prices, the M3 Ultra Mac Studio, and the new M5 / M5 Pro / M5 Max chips are now reflected throughout. Apple discontinued the $599 256GB Mac Mini in May 2026 — the base M4 Mini now starts at $799 with 16GB; the 512GB Mac Studio configuration was pulled in 2026 amid the industry-wide RAM price squeeze.

The tok/s reality: Apple Silicon is slower per token than NVIDIA (an RTX 4090 beats an M4 Max on 7B), because LLM inference is memory-bandwidth bound and Apple's bandwidth is lower. What Apple wins is model capacity per dollar — unified memory lets a $1,799 Mac run 33B models that don't fit on any consumer NVIDIA GPU. The RAM you buy is permanent, so buy more than you think you need.

Apple Silicon changed the calculus for local AI. Unified memory means a $999 Mac Mini with 24GB of RAM can run models that would require a $500 discrete GPU on a PC. No driver headaches. No CUDA compatibility issues. You install Ollama, pull a model, and it works.

But which Mac should you buy? The lineup now spans five generations (M1 through the new M5), with prices from $799 to roughly $10,000 once you load up an M3 Ultra Mac Studio. This guide benchmarks every relevant Apple Silicon chip for AI inference, compares price-per-token across the lineup, and identifies the best buys at different budgets. Before you pick a machine, it helps to know exactly how much memory each model size needs — our RAM requirements for local AI guide breaks down the math model-by-model.

This is not a setup guide. For installation steps, see the Mac local AI setup guide. This is purely about which hardware to buy and why.

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What this guide covers:

• Every Apple Silicon chip ranked for AI inference performance
• Tokens/second benchmarks across 7B, 13B, 33B, and 70B models
• Unified memory explained: why it matters and where it hits limits
• MLX framework performance vs. llama.cpp vs. Ollama
• Price/performance analysis with specific buying recommendations
• Refurbished and used Mac value picks
• Apple Silicon vs. NVIDIA GPU equivalents

Table of Contents

1. How Apple Silicon Runs AI
2. The Complete Chip Comparison
3. Benchmarks: Tokens Per Second
4. Can a Mac Run DeepSeek-R1 671B / Frontier Models?
5. Which Models Fit on Which Mac
6. MLX vs CUDA: Framework Performance
7. Price-Performance Rankings
8. Best Buys by Budget
9. Mac Mini vs MacBook Pro for AI
10. Refurbished and Used Value Picks
11. Apple Silicon vs NVIDIA Equivalents

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How Apple Silicon Runs AI {#how-it-works}

Unified Memory Architecture

On a traditional PC, the CPU has system RAM (DDR5) and the GPU has its own VRAM (GDDR6X). When you load a 14GB model, it must fit entirely in VRAM. If your GPU only has 8GB VRAM, you cannot run that model on the GPU at all.

Apple Silicon eliminates this split. CPU, GPU, and Neural Engine all share a single pool of high-bandwidth memory. A Mac with 32GB unified memory can load a 28GB model and use the GPU for inference without any data copying between memory pools.

The trade-off: Apple's memory bandwidth is lower than dedicated VRAM. An RTX 4090 has 1,008 GB/s bandwidth. The M4 Max tops out at 546 GB/s. Since LLM inference is memory-bandwidth bound (not compute bound), this bandwidth gap directly affects tokens/second. Apple Silicon is slower per-token than equivalent NVIDIA hardware, but it runs models that would not fit on that NVIDIA hardware at all.

Metal GPU Acceleration

Metal is Apple's GPU compute framework, analogous to NVIDIA's CUDA. Ollama, llama.cpp, and the MLX framework all support Metal acceleration. When you run ollama run llama3.2 on a Mac, Metal handles the matrix multiplications on the GPU cores automatically.

Key Metal specs by generation:

How to read that last column — and why it is getting less useful every year. Only four of those TOPS numbers are figures Apple actually printed: 11 for the 16-core M1 Neural Engine (Apple Newsroom, November 2020), 22 for the 32-core M1 Ultra (March 2022), 15.8 for M2 (June 2022) and 31.6 for M2 Ultra (June 2023), plus 38 for M4 (Apple Newsroom, May 2024), which carries across M4 Pro and M4 Max. The commonly quoted "M3 = 18 TOPS" is not an Apple number — Apple's M3 announcement only said the Neural Engine is "60 percent faster" than the M1 family's, and 11 x 1.6 = 17.6, which the internet rounded to 18. The M3 Ultra and the entire M5 family have no published TOPS figure at all: Apple's M3 Ultra page cites a "32-core Neural Engine" with no throughput claim, and the M5 launch moved the AI messaging to the GPU's per-core neural accelerators instead. So any "M5 = 38 TOPS" spec sheet you find is an M4 number copied forward, not something Apple stated. Rows here say "not published" rather than guess, and values prefixed with a tilde in the TFLOPS column are derived from core counts and clock estimates, not Apple figures.

Two important 2026 additions to this table. The M3 Ultra (Mac Studio only) is two M3 Max dies fused over Apple's UltraFusion interconnect into an 80-core GPU — there is no M4 Ultra because the M4 Max chip dropped UltraFusion support, so the M3 Ultra remains Apple's true high-end AI part. The M5 family (base M5 shipped October 2025; M5 Pro and M5 Max arrived March 2026) adds GPU-resident neural accelerators that roughly double matrix-multiply throughput versus M4, and is built on a 3nm-class process. For LLM token generation, though, the M5's real-world gain is modest because generation is still memory-bandwidth bound — the M5's biggest wins show up in prompt processing (time-to-first-token) and image/video models, not raw tok/s on a 70B chat model.

Do not buy a Mac on that TOPS column. Ollama, llama.cpp and MLX all run on Metal — the GPU — so the Neural Engine sits idle during LLM token generation regardless of how many TOPS it claims. The full explanation, including how to confirm it on your own machine, is in does Ollama use the Apple Neural Engine.

For a deeper technical comparison of Metal acceleration versus CUDA, see the MLX vs CUDA for local AI guide. If you are specifically cross-shopping the newest chips, the Apple M5 for local AI guide goes deeper on the M5 Pro and M5 Max than this ranking does.

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The Complete Chip Comparison {#chip-comparison}

Memory Bandwidth: The Real Bottleneck

LLM token generation is memory-bandwidth limited. Each generated token requires reading the entire model weights from memory. Higher bandwidth equals faster token generation, proportionally.

Read this table carefully. The M3 Pro has lower memory bandwidth than the M2 Pro (150 vs 200 GB/s). Apple increased the memory capacity but used a narrower bus. For AI inference, the M2 Pro is actually faster per-token than the M3 Pro on identically-sized models.

The M3 Ultra at 800 GB/s is the outright bandwidth king of the entire lineup — it pushes tokens faster than any other Apple Silicon chip and, critically, scales to 256GB of unified memory (the 512GB option was withdrawn in 2026 during the global RAM shortage). The M5 Max at roughly 614 GB/s is the fastest laptop-class chip and the fastest non-Ultra part, edging past the M4 Max's 546 GB/s by about 12%. So as of mid-2026 the bandwidth order at the top is: M3 Ultra (800) → M5 Max (~614) → M4 Max (546). For desktop AI work where you want both speed and capacity, the M3 Ultra is the chip to beat.

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Benchmarks: Tokens Per Second {#benchmarks}

Where these numbers come from. With one exception, we do not own this hardware, and the per-chip tables below are not first-party measurements. (The exception is the M3 Pro section immediately below, which we measured ourselves and label as such.) The tokens/second figures in the per-chip tables are typical values collated from publicly posted Apple Silicon benchmark runs — principally the long-running llama.cpp Apple Silicon M-series performance thread, where owners post their own results per chip — normalised to Q4_K_M quantization and generation-only throughput (prompt processing excluded). Treat them as the right ballpark and ordering, not a spec. Your own numbers will move with quantization, context length, Ollama/llama.cpp version and thermal state, and short prompts on a cold machine flatter every chip on this page.

What we did measure ourselves: one M3 Pro, 18GB

Everything else on this page is collated from other people's posted runs. This one table is not. These are our own numbers, from a single machine, run on 2026-08-23 with Ollama 0.12.3 on macOS (Darwin 25.5.0, arm64). Each figure is the median of post-warm-up runs; any run generating fewer than 30 tokens was discarded rather than averaged in. Rates are computed from Ollama's raw eval_count and eval_duration via its HTTP API, not scraped from terminal output. The script is in our repo at scripts/benchmark-ollama.py if you want to reproduce it on your own Mac.

One machine is not a survey, so read this as a single honest data point, not a spec.

The one row worth staring at. llama3.2:1b at Q8_0 manages 87.0 tok/s, while the larger qwen2.5-coder:1.5b at Q4_K_M manages 92.7. A model with 25 percent more parameters is faster, because quantization decides how many bytes leave memory per token: roughly 1 byte per weight at Q8_0 against roughly 0.5 at Q4_K_M. About 1.2GB of weights per token versus about 0.75GB. On a bandwidth-bound machine the smaller number wins, and parameter count alone tells you very little.

That is the same physics driving the whole page: if you are choosing between a heavier quant of a small model and a lighter quant of a bigger one, the lighter quant of the bigger model is often both smarter and faster. It is also why our 7.6B Q4_0 result of 29.8 tok/s sits sensibly below the 34 tok/s that the community thread reports for a 7B Q4_K_M on this same chip.

Llama 3.2 7B (Q4_K_M, 4.7GB)

Llama 3.1 13B (Q4_K_M, 7.9GB)

Llama 3.1 70B (Q4_K_M, 40GB)

Only Max and Ultra chips have enough memory for the 70B model at Q4_K_M quantization. The model itself uses ~40GB, and you need additional memory for KV cache (context window). At 8K context, budget 44-46GB total.

Can a Mac run DeepSeek-R1 671B or other frontier models? {#frontier-models}

This is the question that pushed the M3 Ultra Mac Studio into the spotlight in 2026, and it is the single biggest reason to consider a giant unified-memory Mac over a PC. The full DeepSeek-R1 671B (4-bit) model needs roughly 400GB of weights plus headroom — around 448GB of unified memory allocated — which means only the largest Mac Studio configurations can hold it entirely in memory. Third-party hands-on reviews published after the M3 Ultra launch report the Mac Studio running DeepSeek-R1 671B (4-bit) at roughly 16–18 tokens/second at well under 200W of system draw — a frontier-size reasoning model running locally, silently, off a wall socket, with no multi-GPU rig. That is a reviewer-reported figure we have not independently verified, and it moves with quantization and context length, so treat it as an order of magnitude rather than a spec.

Two caveats worth understanding. First, DeepSeek-R1 and Qwen 3's largest variants are Mixture-of-Experts (MoE) models — only a fraction of their parameters activate per token, which is exactly why a bandwidth-bound Mac can serve 671B "active-light" weights faster than the raw parameter count suggests. A dense 671B model would be far slower. Second, Apple pulled the 512GB Mac Studio option in 2026 as DRAM prices spiked, so the comfortable 671B configuration is harder to buy new today; the 256GB M3 Ultra runs it but with very little spare memory for long context. If your goal is the absolute largest local models, buy memory the moment a high-capacity configuration is in stock.

For most readers this is overkill — a 70B or a strong 30B model covers nearly every real task — but it is the clearest demonstration of Apple's core advantage: unified memory lets a single Mac hold models that would otherwise demand a server full of GPUs. If you instead want frontier-class speed on a budget, a dual-GPU NVIDIA 70B build (3090 vs 5090) is the PC-side alternative worth weighing.

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Which Models Fit on Which Mac {#model-capacity}

The rule of thumb: a Q4_K_M quantized model uses roughly 60% of its parameter count in GB. A 7B model needs ~4.7GB, a 13B needs ~7.9GB, a 33B needs ~19GB, and a 70B needs ~40GB. You need additional headroom for macOS (3-5GB), KV cache, and applications.

Memory advice: Buy the most memory you can afford. You cannot upgrade Apple Silicon memory after purchase. Models keep getting bigger, and the memory you think is "overkill" today becomes "barely enough" in two years.

For detailed RAM sizing across all model families, see the RAM requirements for local AI guide.

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MLX vs CUDA: Framework Performance {#mlx-vs-cuda}

Apple's MLX framework is purpose-built for Apple Silicon. It uses unified memory natively and avoids the overhead of adapting CUDA-focused code to Metal. Across publicly posted head-to-head runs, MLX typically lands 10-25% ahead of llama.cpp/Ollama on the same Mac. The table below is the same community-sourced data described above, not our own bench.

Framework Comparison on M4 Max 64GB

MLX is faster because it was designed from scratch for unified memory. It avoids unnecessary memory copies and uses Metal compute shaders optimized for the specific GPU core counts in each chip.

When to use each:

• Ollama: Best ecosystem, model library, API compatibility. Use for most applications.
• MLX: Maximum performance on Apple Silicon. Use when tokens/second matters.
• llama.cpp: Cross-platform compatibility. Use if you also work on Linux/Windows.
• LM Studio: GUI convenience with built-in model management.

For a comprehensive comparison, see the MLX vs CUDA deep dive.

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Price-Performance Rankings {#price-performance}

This is where the analysis gets interesting. We divide each chip's Llama 3.2 7B tokens/second by the machine's starting price to get a tokens/second per $1,000 spent metric.

Price-Performance Table (New, Current Apple Pricing)

The Mac Mini M4 ($799) dominates price-performance by a wide margin. Even after Apple discontinued the old $599 256GB SKU in May 2026 and reset the base price to $799/16GB, at ~41 tokens/second per $1,000 spent it still delivers the best value of any option. The 16GB memory limits you to 7B-13B models, but for those model sizes, nothing beats it on dollars-per-token.

The Mac Mini M4 Pro with 48GB ($1,799) is the best overall value for serious AI work. It runs 33B models comfortably, handles 70B at reduced quality, and still costs less than a gaming GPU + PC build with equivalent model capacity.

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Best Buys by Budget {#best-buys}

Under $1,000: Mac Mini M4 16GB ($799)

This is the entry point. You get M4 performance (33 tok/s on 7B), enough memory for Llama 3.2 7B and Mistral 7B, and a silent, tiny form factor. Pair it with any monitor you already own. (Apple retired the old $599 256GB configuration in May 2026, so $799 with a 512GB SSD is now the floor.)

What it runs well: 3B-7B models at high quality, 13B models at Q3 quantization What it struggles with: Anything over 13B. With only 16GB shared between macOS and models, you hit swap quickly.

Upgrade path: Apple offers 24GB on the base M4 Mini for around $999. That extra 8GB is worth it if you can stretch the budget — see the RAM requirements guide for exactly which models that unlocks.

$1,500-2,000: Mac Mini M4 Pro 48GB ($1,799)

The sweet spot. 48GB unified memory runs Llama 3.1 70B at Q2_K quantization (slow but works) and handles 33B models at full Q4_K_M quality. The M4 Pro's 273 GB/s bandwidth generates tokens faster than the M3 Max at lower cost.

What it runs well: Everything up to 33B at high quality. 70B at reduced quality. Ideal for: Developers using AI coding assistants, researchers experimenting with multiple model sizes, anyone who wants headroom for future models.

$3,500+: Mac Studio M4 Max 64GB ($3,499)

For people who need 70B models at full quality or want to run multiple models simultaneously. The M4 Max's 546 GB/s bandwidth makes 70B inference genuinely usable at 12+ tok/s. With 64GB, you can load a 70B model and still have room for a 7B model alongside it.

What it runs well: Everything including 70B Q4_K_M with generous context. Ideal for: Professional AI development, running inference services for a team, or anyone who wants the fastest possible Apple Silicon experience.

$4,000+: Mac Studio M3 Ultra 96-256GB (from $3,999)

This is the no-compromise frontier-model machine. The M3 Ultra's 800 GB/s bandwidth is the highest in the entire Apple lineup, and its memory ceiling (96GB base, configurable to 256GB) is the only path to running models like DeepSeek-R1 671B or Qwen 3 235B entirely in unified memory. There is no M4 Ultra and no M5 Ultra yet — the M4 and M5 Max chips dropped the UltraFusion interconnect — so in mid-2026 the M3 Ultra remains Apple's true AI workstation.

What it runs well: Multiple 70B models at once, 405B at Q2_K, and MoE frontier models (671B at ~16–18 tok/s on the largest configs). Ideal for: Researchers, teams self-hosting an inference endpoint, and anyone who refuses to touch a multi-GPU server. If you would otherwise build a workstation around several discrete cards, compare the total cost against the best GPUs for AI in 2026 before committing — for raw speed at 70B and below, NVIDIA still wins per dollar.

A note on the new M5 chips

The base M5 (Oct 2025) and M5 Pro / M5 Max (March 2026) MacBook Pros add GPU-resident neural accelerators and push the M5 Max to roughly 614 GB/s — making it the fastest laptop for local AI. But for chat-style token generation the upgrade over an M4 Max is incremental (~10–15%), because generation stays memory-bandwidth bound. The M5's real advantages are faster prompt processing and image/video model work. If you already own an M4 Max, there is no urgent reason to jump; if you are buying a new laptop today and want longevity, the M5 Max is the pick.

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Mac Mini vs MacBook Pro for AI {#mini-vs-macbook}

If you only do AI work at a desk, buy a Mac Mini. Same chips, same memory options, $400-800 less, better thermals due to larger chassis, and you can add any display configuration.

If you need portability, the MacBook Pro is your only option for Max-class chips. The MacBook Air is surprisingly capable with M4 and up to 32GB memory, but it throttles under sustained load due to its fanless design. A 10-minute inference run on an Air will be slower than the same run on a Mini or MacBook Pro due to thermal throttling kicking in around minute 3-4.

Thermal throttling impact (community-reported, same sources as above):

The Mac Mini and Mac Studio maintain full performance indefinitely. The MacBook Pro barely throttles thanks to its active cooling. The MacBook Air drops 20% within minutes. For long inference tasks or always-on serving, avoid the Air.

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Refurbished and Used Value Picks {#refurbished}

Apple's Certified Refurbished store offers previous-generation Macs at 15-20% off with full warranty. For AI, older chips are still excellent because model sizes have not changed dramatically.

Best Refurbished Deals (Early 2026)

The refurbished Mac Studio M2 Max with 64GB is an outstanding AI value. It runs 70B models at Q4_K_M, and at ~$2,400 refurbished, it costs less than building a comparable NVIDIA-based PC.

Used market (eBay, Swappa): M1 Max Mac Studios with 64GB sell for $1,400-1,700. That is a remarkable deal for a machine that comfortably runs 33B models and handles 70B at reduced quality. Check Apple's technical specifications page to verify chip configurations when buying used.

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Apple Silicon vs NVIDIA Equivalents {#vs-nvidia}

How does Apple Silicon stack up against discrete NVIDIA GPUs? The comparison is nuanced because they excel at different things.

Raw Performance Comparison

NVIDIA wins on raw tokens/second, often by 2x or more. The RTX 4090 at $1,600 is faster at 7B inference than a $3,500 M4 Max Mac Studio. For a concrete PC-side counterpoint, our cheapest 70B build: dual RTX 3090 vs 5090 breakdown shows where multi-GPU still beats a Mac on price-per-token.

Apple wins on model capacity per dollar. The M4 Pro 48GB Mac Mini ($1,799) can run 33B models that do not fit on any consumer NVIDIA GPU under $1,600. The M2 Ultra 192GB ($6,999) runs 405B models that would require a $30,000+ multi-GPU NVIDIA setup.

When to Choose Apple Silicon

• You need to run models larger than 24GB (the NVIDIA consumer VRAM ceiling)
• You want a silent, power-efficient machine
• You value zero-configuration setup (no driver debugging)
• You are already in the Apple ecosystem
• You need a laptop that runs AI inference

When to Choose NVIDIA

• Maximum tokens/second is your priority
• Your models fit in 24GB VRAM
• You want the cheapest inference per token
• You plan to fine-tune models (CUDA ecosystem is dominant)
• You need multi-GPU scaling for production inference

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Frequently Asked Questions

Is the base M4 Mac Mini good enough for local AI?

The M4 Mac Mini with 16GB ($799, after Apple retired the old $599 256GB SKU in May 2026) runs 7B models at 33 tokens/second. That is fast enough for interactive chat, code completion, and basic summarization. The limitation is memory: you are restricted to 7B models at Q4 quantization, with little headroom for context windows. For about $200 more, the 24GB configuration gives meaningful breathing room.

Should I buy the M4 Pro or M3 Max?

M4 Pro. Despite the M3 Max having more GPU cores, the M4 Pro's higher memory bandwidth per core and improved architecture deliver comparable or better AI inference performance at lower cost. The M3 Max only wins if you specifically need more than 48GB unified memory.

Does the Neural Engine help with LLM inference?

No — Ollama, llama.cpp and MLX are all Metal (GPU) paths, so the ANE stays idle while tokens stream. Full breakdown: does Ollama use the Apple Neural Engine.

Can I upgrade the memory in an Apple Silicon Mac later?

No. Apple Silicon uses unified memory soldered directly to the chip package. The memory configuration you buy is permanent. This makes choosing the right amount critical. For AI, err on the side of more memory. 24GB is the minimum we recommend; 48GB is the sweet spot.

Is an M1 Mac still worth buying for AI in 2026?

An M1 with 16GB remains perfectly usable for 7B model inference at ~22 tokens/second. If you already own one, there is no urgent reason to upgrade unless you need larger models. If you are buying used, an M1 Mac Mini 16GB at $400-500 is an excellent entry point for experimenting with local AI.

What is the best Mac for running 70B models?

The Mac Studio M4 Max with 64GB ($3,499) is the most practical pick. A 70B at Q4_K_M is about 40GB of weights, which leaves room for KV cache and macOS on a 64GB machine, at roughly 12.5 tok/s. Below 64GB you are into Q2_K compromises; above it, you are paying for context headroom rather than speed.

Does the MacBook Air throttle during AI inference?

Yes. The Air is fanless, so sustained generation settles at roughly 79% of its opening tokens/second after a few minutes. For a quick question that never matters. For long transcription runs or an always-on Ollama server, choose a Mini, a Studio or a MacBook Pro — all actively cooled, all hold their speed indefinitely.

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Conclusion

For most people buying a Mac specifically for local AI in 2026, the answer is the Mac Mini M4 Pro with 48GB for $1,799. It runs every model up to 33B at high quality, handles 70B at reduced quality, and costs less than an equivalent NVIDIA-based PC build when you account for the complete system price.

If you are on a tight budget, the base Mac Mini M4 at $799 (16GB) runs 7B-13B models faster than you might expect, and the 24GB step-up (around $999) buys real headroom. If you need the absolute best Apple Silicon experience, the Mac Studio M4 Max with 64-128GB is the fast desktop pick — and for frontier-size models like DeepSeek-R1 671B, only the M3 Ultra (up to 256GB, 800 GB/s) can hold them entirely in memory.

Do not overlook the used market. An M1 Max Mac Studio with 64GB for $1,500 used is still one of the best price-to-model-capacity ratios available in any platform.

The RAM you buy is the RAM you have forever. Buy more than you think you need.

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Ready to set up your new Mac for AI? Follow the Mac local AI setup guide for step-by-step Ollama installation, or check the RAM requirements guide to confirm which models fit your configuration.