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Pick a Mac for local LLMs by unified memory capacity first. A model’s weights have to fit in memory before anything else matters. Memory bandwidth comes second, and it mostly governs how quickly a loaded model generates text. CPU core count is the weakest of the three guides, because two Macs with similar core counts can differ widely in memory ceiling and bandwidth. Apple’s published figures run from a 16GB Mac mini to a Mac Studio with M3 Ultra that reaches 256GB of unified memory and 819GB/s of bandwidth. Those numbers narrow the shortlist. They do not guarantee that a given model, quantization, or context length will fit or run at a particular speed.
Start with memory capacity, because it decides whether a model loads at all
A model’s weights occupy unified memory. The runtime needs its own room on top of that, plus memory for the conversation so far. If the total exceeds what the Mac has, the model does not run usefully, however fast the chip is.
Apple’s clearest published example comes from its 2025 WWDC session, which showed a 670-billion-parameter DeepSeek model quantized to 4.5 bits per weight. Apple said the weights alone need around 380GB. That is a weights-only figure, so the real requirement is higher.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The arithmetic behind that number is parameters multiplied by bits per weight, divided by 8 to get bytes. For 670 billion parameters at 4.5 bits, that gives about 377GB, which matches Apple’s figure. Applying the same weights-only check to common model sizes gives a rough picture. It is not a formula Apple publishes, and it leaves out context memory, runtime allocations, and the differing layouts of real quantized files.
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| Model size | Approximate weights at 4.5 bits per weight | Check against the configurations in this guide |
|---|---|---|
| 8 billion parameters | about 4.5GB | Fits every configuration here, including the 16GB Mac mini with M4. |
| 27 billion parameters | about 15GB | Needs at least the 24GB configuration. The 16GB configuration is too tight. |
| 70 billion parameters | about 39GB | Does not fit in 32GB. Needs at least 48GB, and 64GB or more leaves more room. |
| 670 billion parameters | about 377GB | Exceeds the 256GB ceiling of every single Mac in this guide. |
Two things push real requirements above these weights-only figures: the context window, since longer conversations hold more state in memory, and everything else the Mac is running, including macOS.
Bandwidth sets the speed ceiling, not whether a model fits
Memory bandwidth is how fast the chip can move data between unified memory and its processors. For most dense models, generating each token means reading the weights again, so bandwidth is a major factor in generation speed. It cannot compensate for missing capacity. A Mac whose memory is too small cannot load a model that needs more memory than it has, regardless of bandwidth. Bandwidth matters once the model is in memory.
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Prompt processing and token generation are different phases
Speed claims often blur two phases. Prompt processing reads the text you send in. Token generation produces the reply one token at a time. Apple ties its M5 Neural Accelerator gains to matrix multiplication, which is central to prompt processing. In its 2026 WWDC local-agent session, Apple said M5 Neural Accelerators make matrix multiplication four times faster on M5 than on M4 in its comparison, and that this translates to nearly the same prompt-processing speedup with its MLX kernels. Its March 3, 2026 newsroom announcement says the M5 Pro and M5 Max deliver up to 4x faster LLM prompt processing than the M4 Pro and M4 Max. Both are Apple’s own comparisons in Apple’s own test setups. Neither says generation is four times faster.
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Why bandwidth is a better guide than core count, within limits
Two Macs can share a CPU core count and still differ sharply in memory ceiling and bandwidth. Those differences decide which models load and how quickly they generate, which is the practical case for ranking bandwidth ahead of core count.
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- 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.*
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The claim has limits. Apple’s published specifications do not include same-model benchmarks across these machines. Bandwidth is also not isolated from GPU design, software kernels, quantization, context length, or core count. Treat it as a strong selection input, not a measured ranking of speed.
Reference table: Apple’s published configurations
These are Apple’s published figures for the configurations this guide covers. Memory and bandwidth come from Apple’s product pages for each model, and the dates are those of the cited figures. Availability and exact specifications vary by model and region, so confirm the configuration you plan to order on Apple’s product page.
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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.
- 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.
| Mac and chip | Unified memory (Apple’s published range) | Memory bandwidth | Date of cited figures | Practical note |
|---|---|---|---|---|
| Mac mini, M4 | 16GB standard; configurable to 24GB or 32GB | 120GB/s | 2024 | Entry point for small models; desktop. |
| Mac mini, M4 Pro | 24GB standard; configurable to 48GB or 64GB | 273GB/s | 2024 | Desktop; 48GB or 64GB is needed for 70-billion-parameter weights. |
| MacBook Pro, M5 Pro | Configurable up to 64GB | 307GB/s | 2026 | Portable; 64GB is the memory ceiling for large models. |
| MacBook Pro, M5 Max | Configurable up to 128GB | 460GB/s or 614GB/s depending on GPU configuration | 2026 | Portable; highest bandwidth of the portable options here. Confirm which GPU configuration your figure applies to. |
| Mac Studio, M4 Max | Configurable up to 128GB | 410GB/s or 546GB/s depending on configuration | 2025 | Desktop at the same 128GB ceiling as the M5 Max. Confirm the configuration before comparing bandwidth. |
| Mac Studio, M3 Ultra | 96GB base; configurable to 256GB | 819GB/s | 2025 | Highest memory and bandwidth of any single Mac in this guide. |
How to shortlist a Mac for a local model
- Choose the target model and its quantization. A model’s size alone does not determine its memory needs, because bits per weight changes the result.
- Calculate the weights: parameters multiplied by bits per weight, divided by 8. Treat the result as a floor, not the total.
- Add headroom for the context length you plan to use and for macOS. Context length is the setting your runtime exposes for how much conversation it keeps in memory.
- Find the smallest configuration whose unified memory covers the total. Then check its bandwidth in the table above to set speed expectations, and confirm which GPU configuration that bandwidth figure applies to.
- Decide between one machine and several. A single Mac is simpler. Distributed inference is a separate setup with its own speedup limits, covered below.
- Confirm that your runtime supports both the model format and the Mac you chose. Then run your intended model on it before committing to a configuration.
Which chip runs which model, tier by tier
Each tier starts from its memory ceiling and shows which weights fit and what trade-offs follow.
Up to 32GB: Mac mini with M4
- Suited to models in the 8-billion-parameter class, which need about 4.5GB of weights at 4.5 bits.
- A 27-billion-parameter model needs the 24GB configuration at minimum, and it leaves limited room for longer conversations at that size.
- A 70-billion-parameter model does not fit at 4.5 bits, because its weights alone come to about 39GB.
- Trade-off: it is the smallest desktop in this guide and has the lowest bandwidth in the table, so expect slower generation than on the higher tiers.
48GB to 64GB: Mac mini with M4 Pro and MacBook Pro with M5 Pro
- This tier is where 70-billion-parameter models become practical. Their roughly 39GB of weights fit in 48GB with little headroom and in 64GB with more room for context.
- The Mac mini with M4 Pro is a desktop. The MacBook Pro with M5 Pro is the portable choice, and its 64GB ceiling is the limit for large models on that machine.
- Trade-off: once a model needs more than 64GB, you need the 128GB or 256GB tier.
Up to 128GB: MacBook Pro with M5 Max and Mac Studio with M4 Max
- 128GB holds 70-billion-parameter weights with substantial room for context, and it extends to considerably larger models on weights alone.
- The MacBook Pro with M5 Max is the portable option with the highest bandwidth in the table among portable configurations.
- The Mac Studio with M4 Max offers the same memory ceiling in a desktop. Its bandwidth depends on the configuration you order, so confirm it before comparing it with the M5 Max.
- Trade-off: the memory ceilings match, so the decision comes down to portability versus a desktop setup.
Up to 256GB: Mac Studio with M3 Ultra
- The highest memory ceiling and bandwidth of any single Mac in this guide, which makes it the reference point for the largest single-machine models.
- Weights-only arithmetic at 4.5 bits puts the theoretical ceiling near 450 billion parameters. Real limits are lower once context and the system take their share.
- Trade-off: beyond this ceiling, you need more than one machine.
When one Mac is not enough: distributed inference
Apple’s 2026 WWDC session on distributed MLX shows models sharded across several Macs. Those examples are Apple’s own demonstrations, so treat them as illustrations rather than universal results.
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- FAST RUNS IN THE FAMILY — The 14-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.
- Qwen 3.6, a 27-billion-parameter model: Apple ran it on one M3 Ultra and on four. It reported nearly three times the token-generation rate across four machines, and said the exact speedup depends on model size and architecture.
- Kimi 2.6, a one-trillion-parameter model: Apple said its 8-bit weights alone need about one terabyte. That is more than one M3 Ultra in the demonstration, but it can be distributed across four. Apple presented this as an illustrative claim, not an independently validated result.
Nearly three times the speed across four machines works out to roughly three-quarters of perfect four-way scaling for that one model. Adding machines helps, but the gain depends on the model, and a four-machine setup brings its own configuration work.
Software: MLX and MLX LM
Apple’s own local examples use MLX. Apple describes MLX LM as an open-source Python package built on MLX for running language models locally on Apple silicon. The WWDC26 distributed session shows both command-line and Python API workflows.
Apple’s material does not include a compatibility table for third-party runtimes, so Apple’s examples do not establish how other tools handle the same models or Macs. Check your runtime’s documentation for supported model formats and hardware before assuming a model will load.
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Troubleshooting when a local model misbehaves
- The model will not load. The weights plus runtime allocations exceed available memory. Choose a smaller model, use a more aggressive quantization with fewer bits per weight (which can cost some output quality), or move to a higher memory configuration.
- It loads, but a long conversation fails. Context memory has outgrown the headroom. Reduce the context length your runtime allows, or start a new session.
- It loads, but responses are slow. Work out whether the delay is in prompt processing or token generation. Long pasted text slows prompt processing. Generation speed tracks bandwidth and model size. Compare speeds only on the same model and quantization.
- The Mac is swapping heavily. Open Activity Monitor and check the Memory Pressure graph on the Memory tab. Sustained high pressure means the model and its working memory do not fit comfortably.
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