Apple Silicon AI Benchmarks: How Fast Is Local AI on M-Series Macs in 2026?

Apple Silicon — the M1, M2, M3, M4, and now M5 series of chips — has quietly become one of the most efficient platforms for running local large language models. Not because Apple designed it specifically for AI (it didn't), but because the unified memory architecture, on-chip GPU, and Metal acceleration framework happen to be unusually well-suited to LLM inference. Here is what measured benchmarks actually show, chip by chip.

How Local LLM Performance Is Measured

The standard metric is tokens per second (tok/s) — how many word-fragments the model can generate in one second. For context, most people read about four words a second, which is roughly five tokens. A model generating 20 tok/s or more streams text faster than you can read it.

The other number that matters is prompt processing: how fast the model reads what you give it before it writes the first word. For a short question this is almost instant. For a long document it is the wait you notice, and it varies enormously by chip.¹

The Spec That Matters Most: Memory Bandwidth

When a model generates text, every new token requires reading the model's weights out of memory. So generation speed tracks memory bandwidth — how many gigabytes per second the chip can move — more closely than CPU cores, GPU cores or Neural Engine specs. A useful rule of thumb: maximum generation speed is roughly bandwidth divided by the size of the model in memory.

The clearest evidence is the M3 Pro. Apple cut its bandwidth to 150 GB/s from the M2 Pro's 200 GB/s, and it generates text more slowly than the chip it replaced: 31 tok/s against 39.¹

M1, M1 Pro, M1 Max, M1 Ultra (2020–2022)

• M1 (68 GB/s): ~14 tok/s.
• M1 Pro (200 GB/s): ~36 tok/s.
• M1 Max (400 GB/s): ~61 tok/s.
• M1 Ultra (800 GB/s): ~84 tok/s.

The M1 Pro and Max are still quick generators. Most base M1 machines shipped with 8 GB of RAM, which limits them to small models.

M2, M2 Pro, M2 Max, M2 Ultra (2022–2023)

• M2 (100 GB/s): ~22 tok/s.
• M2 Pro (200 GB/s): ~39 tok/s.
• M2 Max (400 GB/s): ~66 tok/s.
• M2 Ultra (800 GB/s): ~94 tok/s.

The base M2 gained the most over its predecessor, because its bandwidth rose. The Pro and Max chips kept the same bandwidth and landed within about 10% of their M1 equivalents.¹

M3, M3 Pro, M3 Max (2023–2024)

• M3 (100 GB/s): ~21 tok/s.
• M3 Pro (150 GB/s): ~31 tok/s — slower than the M2 Pro.
• M3 Max (400 GB/s, 40-core GPU): ~66 tok/s.

M4, M4 Pro, M4 Max (2024–2025)

• M4 (120 GB/s): ~24 tok/s.
• M4 Pro (273 GB/s): ~51 tok/s.
• M4 Max (546 GB/s, 40-core GPU): ~83 tok/s.

M5, M5 Pro, M5 Max, M5 Ultra (2025–2026)

The base M5 arrived in October 2025 with 153 GB/s of memory bandwidth, about 30% more than the M4.² The M5 Pro and M5 Max followed in March 2026, with up to 307 GB/s and 614 GB/s respectively.³ In August 2026 Apple announced the M5 Ultra for Mac Studio.⁴

• M5 Max (614 GB/s, 40-core GPU): ~120 tok/s — the fastest generator in the llama.cpp results, and far faster at reading prompts in: about 3,200 tok/s, against 886 tok/s on the M4 Max.¹
• M5 and M5 Pro: no comparable llama.cpp results yet. Going by bandwidth, expect the M5 a little ahead of the M4, and the M5 Pro a little ahead of the M4 Pro.

What These Numbers Mean for Real Work

Numbers are abstract. Here is what the base M4 (24 tok/s) and the M4 Max (83 tok/s) feel like on everyday tasks with a 7B model:

Drafting a 300-word client email: roughly 400 tokens of output. About 17 seconds on a base M4; about 5 seconds on an M4 Max.

Summarizing a 2,000-word document: the document is roughly 2,700 tokens to read in, then maybe 200 tokens of summary. On a base M4, which reads prompts at about 221 tok/s, that is around 12 seconds before the first word and 8 more to finish. On an M4 Max, about 3 seconds and 2 more.¹

Be clear-eyed about the comparison with cloud AI: the big hosted models usually start and finish faster than a laptop, especially on long documents. The reason to run locally is where your data goes, not speed.

The Hidden Variable: Quantization

Models are usually run quantized — compressed to fewer bits per weight. For an 8B model, llama.cpp lists the full-precision (F16) file at about 15 GB, Q8_0 at 8 GB, Q5_K_M at 5.3 GB and Q4_K_M at 4.6 GB.⁵ Lower-bit versions lose some quality, which is why 4- and 5-bit K-quants are the usual compromise; Ollama's default downloads are 4-bit.

Quantization changes speed as much as size. On a base M4, the same 7B model generates about 7 tok/s at F16, 14 tok/s at Q8_0, and 24 tok/s at Q4_0.¹

How to Pick the Right Mac for Local AI

If you're buying a Mac with local AI in mind:

Budget tier: any current Mac with 16 GB of RAM runs 7B–8B models comfortably. The base chips have the lowest bandwidth, so they are the slowest generators.

Daily driver: a Pro chip with 24 GB or more. Roughly double the base chip's generation speed, and room for 14B models.

Power user: a Max chip with 64 GB or more. Fast generation, and enough memory for 70B-class models, which take about 40 GB at 4-bit.

The used-Mac shortcut: an M1 Max or M2 Max with 32 GB generates text more than twice as fast as a new base M4, because of its 400 GB/s bandwidth.¹

For detailed hardware guidance, see our Mac Hardware Requirements guide. For model recommendations once you have the right Mac, see the Best Local LLMs for Mac.

Where Apple Silicon Still Lags

Apple Silicon is excellent for inference (running models) but less competitive for training (creating models). NVIDIA workstation GPUs remain dominant for actual model training and fine-tuning, especially at large scales.

Apple is also still catching up on certain niche LLM formats and acceleration techniques. MLX (Apple's ML framework) and llama.cpp's Metal backend are the well-supported paths; some cutting-edge model releases appear first for NVIDIA hardware and take days or weeks to gain Metal support.

Part of our On-Device AI cluster: See the pillar guide for the full picture, or jump to how to set up Ollama on your Mac.