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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe best local AI model for your Mac depends on unified memory more than on chip generation. As a starting point: a small 4-bit model on 8GB, an 8B 4-bit model on 16GB, a 14B 4-bit or 20B-class low-precision model on 24GB, and a 30B mixture-of-experts (MoE) 4-bit model on 32GB or more. Above that, extra memory buys context length and headroom rather than a fixed model ceiling.
Only some of those tiers have direct measurements. Apple Machine Learning Research published inference memory figures for several models on a 24GB M5 MacBook Pro (2025). The other tiers are planning advice inferred from those numbers, and this article labels which is which.
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Quick answer: which model size fits which Mac
| Unified memory | Sensible starting point | Basis |
|---|---|---|
| 8GB | Small models (around 4B and under) at 4-bit, short context | Inferred: Apple measured an 8B 4-bit model at 5.61GB, which leaves little room on 8GB |
| 16GB | 8B at 4-bit | Inferred from the 5.61GB measurement; not tested on a 16GB machine |
| 24GB | 14B at 4-bit (9.16GB); GPT-OSS 20B in MXFP4 (12.08GB); 30B-A3B MoE at 4-bit (17.31GB) is tight but measured | Measured by Apple on a 24GB MacBook Pro |
| 32GB | 14B or 20B-class comfortably; 30B MoE 4-bit with more headroom than at 24GB | Inferred from Apple’s sub-18GB workloads |
| 48GB–64GB | More headroom for larger weights, longer context, and other open apps | No measured “largest model” figure published for these tiers |
| 128GB | Large local-model experiments; still not enough for every model | Mac Studio M5 Max configuration; no published maximum model size |
Why parameter count alone misleads
Apple silicon uses unified memory: CPU and GPU draw on one physical pool. Model weights therefore compete with macOS, your apps, and the prompt and context state for the same gigabytes. As Apple’s MLX engineer Angelos put it in a WWDC25 session, MLX “utilizes Metal for acceleration on the GPU and takes advantage of unified memory so that operations on the CPU and GPU can work on the same data simultaneously.”
Precision matters as much as size. Apple explains that going from 32-bit to 16-bit halves a model’s memory needs, and 4-bit quantization cuts them further. Architecture matters too, as the measurements below show.
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What Apple actually measured
Apple Machine Learning Research (2025) reported these inference memory figures on an M5 MacBook Pro with 24GB, using MLX, a 4096-token prompt and 128 generated tokens:
| Model | Precision | Type | Memory |
|---|---|---|---|
| Qwen3-1.7B | BF16 | Dense | 4.40GB |
| Qwen3-8B | 4-bit | Dense | 5.61GB |
| Qwen3-8B | BF16 | Dense | 17.46GB |
| Qwen3-14B | 4-bit | Dense | 9.16GB |
| gpt-oss-20b | MXFP4 | MoE | 12.08GB |
| Qwen3-30B-A3B | 4-bit | MoE | 17.31GB |
Two lessons follow. First, the 30B-A3B MoE model (17.31GB) used about the same memory as the 8B model at BF16 (17.46GB), so the “30B” label says little about fit. A MoE model must still hold all its experts in memory even though only a fraction is active per token. Second, quantizing the 8B model from BF16 to 4-bit cut its footprint by roughly two-thirds.
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- BUCKLE UP — Along with a next-generation CPU, faster unified memory, and up to 2x faster SSD storage,* M5 Pro and M5 Max feature a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance and on-device training capabilities. So you can blaze through demanding workloads at mind-bending speeds.
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These are Apple’s own figures for one setup, not independent repeats. Longer prompts, different runtimes and other model variants will change them. Apple stated that both the 8B BF16 and the 30B MoE 4-bit workloads stayed under 18GB on the 24GB machine.
Tier-by-tier guidance
8GB: small models, short context
This is mostly an existing-Mac tier: the current MacBook Air M5 and Mac mini M4 configurations Apple lists start at 16GB. Apple’s 8B 4-bit model needed 5.61GB on a machine with plenty of spare memory. On 8GB, macOS and a browser could consume most of what remains, so choose models around 4B parameters or smaller at 4-bit and keep prompts short. Apple’s 2026 local-agent session shows a Qwen 3.5 4B 8-bit model served through MLX-LM, but it publishes no memory figure for it, so treat that as a demonstration rather than a fit guarantee.
Rank #3
- FAST RUNS IN THE FAMILY — The 16-inch MacBook Pro with the M5 Pro or M5 Max chip brings next-generation speed and powerful on-device AI to personal, professional, and creative tasks. With all-day battery life, double the starting storage,* and a breathtaking Liquid Retina XDR display, it’s pro in every way.*
- BUCKLE UP — Along with a next-generation CPU, faster unified memory, and up to 2x faster SSD storage,* M5 Pro and M5 Max feature a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance and on-device training capabilities. So you can blaze through demanding workloads at mind-bending speeds.
- BUILT FOR AI — Apple silicon, and every major component that powers it, is designed to run demanding on-device AI workloads like LLM inference and training. And Apple Intelligence helps you write, express yourself, and get things done effortlessly with groundbreaking privacy protections at every step.*
- ALL-DAY BATTERY LIFE — MacBook Pro delivers the same exceptional performance whether it’s running on battery or plugged in.*
- MACOS RUNS APPS FAST — All your go-to apps run lightning fast in macOS, including built-in apps like FaceTime and Messages. Plus, built-in virus protection and free software updates help keep your Mac running smoothly and securely.
16GB: 8B at 4-bit
An 8B 4-bit model (5.61GB in Apple’s test) is the reasonable default, leaving several gigabytes for macOS, the app and context. Close memory-hungry apps before long-context sessions. A 14B 4-bit model (9.16GB) is a possible stretch, but with less margin; this is inference, not a tested 16GB result.
24GB: the best-documented tier
Apple’s measurements were made here. The 14B 4-bit model and GPT-OSS 20B in MXFP4 sit comfortably at 9–12GB. The 30B-A3B MoE at 17.31GB fits but leaves modest room, so avoid very long contexts and heavy multitasking. Treat that result as specific to that model and workload, not as proof that any 30B model fits.
Rank #4
- SUPERCHARGED BY M5 — The 14-inch MacBook Pro with M5 brings next-generation speed and powerful on-device AI to personal, professional, and creative tasks. Featuring all-day battery life and a breathtaking Liquid Retina XDR display with up to 1600 nits peak brightness, it’s pro in every way.*
- HAPPILY EVER FASTER — Along with its faster CPU and unified memory, M5 features a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance. So you can blaze through demanding workloads at mind-bending speeds.
- BUILT FOR APPLE INTELLIGENCE — Apple Intelligence is the personal intelligence system that helps you write, express yourself, and get things done effortlessly. With groundbreaking privacy protections, it gives you peace of mind that no one else can access your data — not even Apple.*
- ALL-DAY BATTERY LIFE — MacBook Pro delivers the same exceptional performance whether it’s running on battery or plugged in.
- APPS FLY WITH APPLE SILICON — All your favorites, including Microsoft 365 and Adobe Creative Cloud, run lightning fast in macOS.*
32GB: more headroom for the same models
The 24GB-tier models run with more margin, and a 30B MoE 4-bit model becomes a comfortable choice rather than a tight one. The MacBook Air M5 can be configured to 32GB, as can Mac mini M4 variants. This is extrapolated from Apple’s sub-18GB figures, not separately tested.
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48GB and 64GB: room to scale context and multitask
Mac mini M4 configurations reach 64GB, and Mac Studio M5 Max offers 48GB and 64GB. Apple publishes no maximum-model recommendation for these capacities, so avoid fixed claims. Practically, the extra memory lets you keep a mid-sized model loaded alongside your normal work, use longer contexts, or try higher-precision variants of the models above.
Best Value
- SUPERCHARGED BY M5 — The 14-inch MacBook Pro with M5 brings next-generation speed and powerful on-device AI to personal, professional, and creative tasks. Featuring all-day battery life and a breathtaking Liquid Retina XDR display with up to 1600 nits peak brightness, it’s pro in every way.*
- HAPPILY EVER FASTER — Along with its faster CPU and unified memory, M5 features a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance. So you can blaze through demanding workloads at mind-bending speeds.
- BUILT FOR APPLE INTELLIGENCE — Apple Intelligence is the personal intelligence system that helps you write, express yourself, and get things done effortlessly. With groundbreaking privacy protections, it gives you peace of mind that no one else can access your data — not even Apple.*
- ALL-DAY BATTERY LIFE — MacBook Pro delivers the same exceptional performance whether it’s running on battery or plugged in.
- APPS FLY WITH APPLE SILICON — All your favorites, including Microsoft 365 and Adobe Creative Cloud, run lightning fast in macOS.*
128GB: large experiments, with limits
Mac Studio M5 Max can be configured with 128GB, and M5 Ultra configurations range from 96GB to 512GB. Runtime overhead and context still come out of that pool. For scale, in WWDC25 Apple showed a 670-billion-parameter DeepSeek model quantized to 4.5 bits per weight needing around 380GB for weights alone, which it ran on an M3 Ultra with 512GB. A 2026 Apple session also shows a 122B-A3B 8-bit Qwen model launched across multiple Macs with distributed inference, not on a single machine. So 128GB is substantial, but some models need more memory or several Macs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Practical rules for choosing
- Check the actual download size of the quantized file, then add headroom for macOS, apps, and context. Longer prompts use more memory than Apple’s 4096-token test.
- Prefer 4-bit builds on smaller tiers. Use BF16 only when memory is plentiful, since the 8B model’s footprint tripled at that precision.
- Compare MoE and dense models by their file size, not their headline parameter count.
- MLX is Apple’s open-source framework for Apple silicon, and MLX-LM handles loading, generation, quantization and fine-tuning of language models. Community-converted MLX versions of popular models are available through the mlx-community collection.
- External storage does not add unified memory. If you are buying, memory is fixed at purchase, so choose the tier for the models you want to run.
Mac configurations change often. Confirm current memory options on Apple’s site before buying.
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