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 the benchmarks actually look like in 2026, 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: human reading speed is roughly 4–7 tokens per second. Anything above 10 tok/s feels responsive. Anything above 30 tok/s feels instant.

The other relevant metric is time to first token: how long you wait between hitting Enter and seeing the model start responding. For local Apple Silicon, this is typically 15–80 milliseconds — faster than a round-trip to OpenAI's servers.¹

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

The original Apple Silicon line. Still genuinely capable of running local AI in 2026, though increasingly bottlenecked by RAM (most M1 MacBooks shipped with 8 GB).

• M1 (8 GB): ~20 tok/s on 7B-parameter models (Q4 quantization). Comfortable for everyday tasks.
• M1 Pro (16 GB): ~25 tok/s on 7B models; ~12 tok/s on 13B models.
• M1 Max (32 GB): ~28 tok/s on 7B; ~14 tok/s on 13B; can run 30B+ models slowly.
• M1 Ultra (64+ GB): Comparable speeds, can comfortably run 70B models.

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

Roughly 15–25% faster than the M1 line on LLM workloads, with improved memory bandwidth and a more powerful Neural Engine.

• M2 (8–24 GB): ~25 tok/s on 7B models.
• M2 Pro (16–32 GB): ~32 tok/s on 7B; ~16 tok/s on 13B.
• M2 Max (32–96 GB): ~35 tok/s on 7B; ~18 tok/s on 13B; ~6 tok/s on 70B.
• M2 Ultra (64–192 GB): Can run 100B+ models. ~10 tok/s on 70B.

M3, M3 Pro, M3 Max (2023–2024)

The first generation with hardware-accelerated ray tracing and significantly improved GPU performance. Real-world LLM throughput improvements over M2: 15–30%.

• M3 (8–24 GB): ~30 tok/s on 7B.
• M3 Pro (18–36 GB): ~36 tok/s on 7B; ~18 tok/s on 13B.
• M3 Max (36–128 GB): ~42 tok/s on 7B; ~22 tok/s on 13B; ~8 tok/s on 70B.

M4, M4 Pro, M4 Max (2024–2025)

The current mainstream Apple Silicon as of 2026. M4 introduced significant memory bandwidth improvements and a more capable Neural Engine.

• M4 (16–32 GB): ~33 tok/s on 7B; ~17 tok/s on 13B.
• M4 Mac Mini (16 GB): ~33 tok/s on 7B; ~45 tok/s on optimized smaller 20B models with aggressive quantization.
• M4 Pro (24–48 GB): ~40 tok/s on 7B; ~20 tok/s on 13B; ~12 tok/s on 30B.
• M4 Max (36–128 GB): ~48 tok/s on 7B; ~26 tok/s on 13B; ~10 tok/s on 70B.

M5, M5 Pro, M5 Max (2025–2026)

The newest generation, with memory bandwidth jumping from 546 GB/s on M4 Max to 600 GB/s on M5 Max. That bandwidth increase translates almost directly into higher LLM throughput.¹

• M5 (16–48 GB): ~40 tok/s on 7B.
• M5 Pro (24–48 GB): ~48 tok/s on 7B; ~25 tok/s on 13B.
• M5 Max (36–128 GB): ~55 tok/s on 7B; ~30 tok/s on 13B; ~12 tok/s on 70B; ~158 tok/s on small Gemma 4 models.

What These Numbers Mean for Real Work

Numbers are abstract. What does 33 tok/s actually feel like when you're working?

Drafting a 300-word client email: Roughly 400 tokens of output. At 33 tok/s, the response completes in about 12 seconds. The response starts appearing within 50 ms of pressing Enter.

Summarizing a 2,000-word document: Maybe 200 tokens of output. At 33 tok/s, six seconds.

Explaining a confusing tax clause: A few hundred tokens. Done before you finish reading the question back to yourself.

For comparison, ChatGPT typically takes 70–190 ms just for network round-trip before the model even starts generating. Local AI on Apple Silicon doesn't feel faster because the model is faster — it feels faster because there's no network latency.²

The Hidden Variable: Quantization

All the benchmarks above assume Q4 or Q5 quantization — common formats that compress models to roughly 4–5 bits per parameter while preserving 95–98% of the original model's quality.

If you run an unquantized (FP16) model, expect 2–3x slower throughput but marginally better quality. For most professional tasks, the quality difference is imperceptible and the speed difference is significant. Q4_K_M and Q5_K_M are the recommended formats for everyday Mac use.

How to Pick the Right Mac for Local AI

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

Budget tier: M4 Mac Mini with 16 GB RAM ($799). Runs 7B models comfortably, 13B with some patience. Excellent value.

Daily driver: MacBook Pro M4 Pro with 24+ GB RAM. Runs 13B comfortably and can handle 30B models for harder tasks.

Power user: MacBook Pro M4 Max or M5 Max with 64 GB+ RAM. Runs 70B models at usable speeds. Future-proof for the next two years of model releases.

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. As of mid-2026, MLX (Apple's ML framework) and llama.cpp's Metal backend are the well-supported paths; some cutting-edge model releases appear first as CUDA-only 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.