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If you are choosing a Mac for local models, start with unified memory, not CPU core count. Memory capacity decides whether a model’s weights can load at all. Memory bandwidth then governs how quickly the loaded model generates output. Across the Apple configurations cited here, the range runs from a Mac mini with M4 at up to 32GB and 120GB/s to a Mac Studio with M3 Ultra at up to 256GB and 819GB/s. Those two numbers tell you more about what you can run than the CPU core count does.
Start with the memory ceiling
Work through these steps in order when you shortlist a Mac for local models:
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- List the models you need by parameter count and quantization. A 70-billion-parameter model at 4 bits per weight is a different hardware purchase from the same model at 8 bits.
- Estimate the weight size. Multiply the parameter count by the bits per weight, then divide by eight. A 70B model at 4 bits works out to about 35GB; at 8 bits, about 70GB. Treat the result as a floor, not a fit test.
- Add memory for the runtime, the context window and macOS. Apple’s published figures do not quantify this overhead, so leave generous headroom above the weight figure.
- Filter Macs by their maximum configurable unified memory, using the tables below. A machine whose memory ceiling is below your total fails the test, however fast it is.
- Among the machines that pass, compare memory bandwidth against the speed you expect.
- Confirm the software path. Apple’s own local-model examples use MLX and the MLX LM package, so check that your model and tooling run there.
- Decide whether one Mac is enough. Apple has demonstrated splitting one model across several Macs, covered below.
Which Mac configurations exist
The table lists configurations from Apple’s product specifications, published between 2024 and 2026. Memory ceilings are options you select, not defaults. The bandwidth a machine reports can depend on its GPU option. Confirm the exact figures on Apple’s product page for the model and region you are buying.
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|---|---|---|---|
| Mac mini, M4 | 16GB standard; configurable to 24GB or 32GB | 120GB/s | Apple product specifications, 2024 |
| Mac mini, M4 Pro | 24GB standard; configurable to 48GB or 64GB | 273GB/s | Apple product specifications, 2024 |
| MacBook Pro, M5 Pro | Configurable up to 64GB | 307GB/s | Apple product specifications, 2026 |
| MacBook Pro, M5 Max | Configurable up to 128GB | 460GB/s or 614GB/s, depending on GPU configuration | Apple product specifications, 2026 |
| Mac Studio, M4 Max | Configurable up to 128GB | 410GB/s or 546GB/s, depending on configuration | Apple product specifications, 2025 |
| Mac Studio, M3 Ultra | 96GB base; configurable up to 256GB | 819GB/s | Apple product specifications, 2025 |
Two patterns stand out. Memory ceilings step from 32GB to 64GB to 128GB to 256GB across these machines. The top bandwidth figures follow a similar ladder: the M5 Max MacBook Pro’s 614GB/s is twice the M5 Pro’s 307GB/s, and the M3 Ultra Mac Studio’s 819GB/s is about 1.5 times the M4 Max’s top figure of 546GB/s.
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Which chip runs which model
Map the model size to a memory tier first, then decide how much bandwidth you want within that tier. The weight sizes below use the parameters-times-bits arithmetic from the first section. They are approximations, not Apple test results.
| Model (weights only) | Quantization | Approximate weight size | Lowest cited single-Mac ceiling that holds the weights |
|---|---|---|---|
| 27B-parameter model (Qwen 3.6 27B class) | 4-bit | about 13.5GB | 16GB Mac mini with M4: possible on paper, but little headroom |
| 27B-parameter model (Qwen 3.6 27B class) | 8-bit | about 27GB | 32GB Mac mini with M4: possible on paper, but little headroom |
| 70B-parameter model | 4-bit | about 35GB | 64GB: Mac mini with M4 Pro or MacBook Pro with M5 Pro |
| 70B-parameter model | 8-bit | about 70GB | 128GB: Mac Studio with M4 Max or MacBook Pro with M5 Max |
| 670B-parameter DeepSeek model (Apple WWDC25 example) | 4.5-bit | about 377GB by arithmetic; Apple states around 380GB for weights alone | None of the cited single Macs; the largest cited ceiling is 256GB |
| One-trillion-parameter Kimi 2.6 (Apple WWDC26 example) | 8-bit | about 1TB; Apple’s figure for weights alone | None as a single machine in Apple’s demonstration; Apple distributed it across four M3 Ultra systems |
Among single machines cited here, the 256GB Mac Studio with M3 Ultra leaves the most room for large models and long contexts. Its 96GB base configuration is a different purchase, so select the memory option deliberately.
Why capacity comes first
Capacity is a hard gate. If the weights plus working memory do not fit, the model will not load, and neither bandwidth nor core count can rescue it. Apple’s WWDC25 session showed a 670-billion-parameter DeepSeek model quantized to 4.5 bits per weight and stated that its weights alone need around 380GB. Multiplying out the parameters and bit width gives about 377GB, which is consistent with Apple’s figure. Runtime allocations and context memory sit on top of the weights, so Apple’s number is a weight figure, not a total runtime requirement.
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Quantization belongs in the fit check. The same model at 4 bits and at 8 bits differs by a factor of two in weight memory, so a parameter count on its own does not tell you whether a Mac can run it.
Why bandwidth, not core count, sets speed
Core count does not enter the fit test. Whether a model loads depends on memory, and the specifications cited here use memory and bandwidth as their headline figures for local models. Core count is not a useful proxy for either.
Bandwidth matters once the model is loaded. For a dense model, generating each token generally requires reading most of the weights from memory, so memory bandwidth sets a rough ceiling on token generation. That is a mechanism, not a measurement: the sources cited here do not include a controlled benchmark that compares these Macs on the same model and quantization.
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Keep prompt processing and token generation separate. Prompt processing is dominated by matrix multiplication, which is where Apple’s M5 Neural Accelerators apply. Token generation is where bandwidth matters most.
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What Apple has demonstrated on speed
Prompt processing on M5
Apple’s March 3, 2026 announcement says the M5 Pro and M5 Max deliver up to 4x faster LLM prompt processing than the M4 Pro and M4 Max. Apple’s WWDC26 local-agent session describes M5 Neural Accelerators making matrix multiplication four times faster on M5 than on M4 in its comparison, which it says translates to nearly the same prompt-processing speedup with its MLX kernels. Both are Apple’s figures for prompt processing, not general generation-speed measurements.
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One Mac versus four: Qwen 3.6 27B
In its WWDC26 distributed MLX session, Apple demonstrated Qwen 3.6, a 27-billion-parameter model, on one M3 Ultra and then on four. Apple reports nearly three times the token-generation rate on four machines and says the exact speedup depends on model size and architecture. The result describes Apple’s demonstrated setup. It is not a general multiplier for other models or other Macs.
Kimi 2.6 across four machines
The same session says a one-trillion-parameter Kimi 2.6 model needs about one terabyte for its 8-bit weights alone, more than one M3 Ultra in the demonstration, and that it can be spread across four. This is Apple’s illustrative claim rather than an independently validated result, but it shows how sharding works in practice.
Software: MLX and MLX LM
Apple describes MLX LM as an open-source Python package for running language models locally on Apple silicon. It is built on MLX and offers command-line and Python API workflows. Apple’s local examples use it, so it is the setup to start with if you want to match what Apple demonstrated.
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Choosing by use case
Rank your shortlist against these five axes:
- Maximum unified memory for your target model and context length.
- Memory bandwidth for the inference speed you expect.
- GPU and Neural Accelerator support, and the software kernels your runtime uses.
- Portability against a desktop machine that stays on the desk.
- Single-Mac inference against distributed inference across several Macs.
Experimenting with smaller quantized models
The Mac mini with M4, at up to 32GB and 120GB/s, is the entry point among the cited configurations. It suits smaller quantized models and learning the tooling. A 70B model at 4 bits, at about 35GB of weights, will not fit within its ceiling.
70B-class models at 4 bits
The 64GB tier is the first to hold a 70B model at 4 bits with room to spare. The Mac mini with M4 Pro (273GB/s) is the desktop choice, and the MacBook Pro with M5 Pro (307GB/s) is the portable one. Because their bandwidth differs by only about 12%, form factor is likely to decide between them.
70B models at 8 bits or with long contexts
The 128GB tier opens higher-precision 70B models. The Mac Studio with M4 Max offers 410GB/s or 546GB/s depending on configuration. The MacBook Pro with M5 Max offers 460GB/s or 614GB/s. At the top GPU option, the portable M5 Max’s 614GB/s exceeds the desktop M4 Max’s 546GB/s, so the portable machine can be the faster choice if you order the higher GPU option.
The largest single machine
The Mac Studio with M3 Ultra, configurable to 256GB with 819GB/s, is the highest-capacity and highest-bandwidth single Mac among the configurations cited here. Its 96GB base is only the starting point, so order the memory you need.
Several Macs
If the model you want exceeds every single machine in the tables, the question changes from which Mac to how many. Apple’s examples show distributed MLX across four M3 Ultra systems. The cited sources cover that capability, not the networking or operational setup it requires.
Quick Recap
What these figures do not establish
- Tokens-per-second figures for any Mac. The speed results above are Apple’s demonstrations, tied to the model, quantization and hardware shown.
- Full model compatibility. Apple’s published material does not provide a complete list of models or quantizations that run on each Mac.
- Third-party runtime support. Coverage here is limited to Apple’s MLX and MLX LM examples.
- Pricing, regional availability or current stock.
- Core-count effects on speed. CPU core counts were not measured in these sources, so any speed difference between two Macs cannot be attributed to cores from these figures.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
