Apple Silicon for AI: M4 vs M3 vs M2 Comparison (2026)
Compare M4, M3, and M2 Apple Silicon chips for local AI inference. Unified memory advantages, performance benchmarks, and which Mac to buy for running LLMs and image models.
Apple Silicon has changed what's possible for local AI on a laptop or desktop. Unified memory — the architecture that pools GPU and CPU memory into one shared pool — lets Apple Silicon Macs run AI models far larger than any discrete GPU VRAM allows. An M4 Max with 128 GB can load models that require a $10,000 professional GPU on the NVIDIA side.
But not all Apple Silicon is equal. The gap between an M2 in a MacBook Air and an M4 Ultra in a Mac Studio is enormous. This guide breaks down what each generation and tier can actually do for AI inference.
Why Apple Silicon Is Different for AI
On a standard PC, a GPU has its own dedicated VRAM — typically 8–24 GB for consumer cards. The CPU has separate system RAM. These are different pools that cannot be mixed. A model must fit entirely in VRAM for GPU-accelerated inference.
Apple Silicon uses a unified memory architecture (UMA): the GPU, CPU, and Neural Engine all access the same physical memory. When you configure a Mac with 64 GB, the GPU can use all of it. A model that would require a $6,000+ professional GPU on a Windows PC loads natively on a $2,500 MacBook Pro M4 Max.
The trade-off: Memory bandwidth. Consumer NVIDIA GPUs like the RTX 4090 offer 1 TB/s of bandwidth. Apple Silicon's bandwidth is lower (192–820 GB/s depending on chip), meaning tokens-per-second is slower for the same model. Capacity first, speed second.
Generation Overview
M2 Series (2022–2023)
The M2 generation introduced significant memory bandwidth improvements over M1. Available in four tiers: M2, M2 Pro, M2 Max, M2 Ultra.
Key AI specs:
- M2: up to 24 GB unified memory, 100 GB/s bandwidth
- M2 Pro: up to 32 GB unified memory, 200 GB/s bandwidth
- M2 Max: up to 96 GB unified memory, 400 GB/s bandwidth
- M2 Ultra: up to 192 GB unified memory, 800 GB/s bandwidth
Neural Engine: 16-core, ~15.8 TOPS
The M2 generation is where Apple Silicon became genuinely compelling for AI. The M2 Max 64 GB configuration was a landmark — it could run 70B models for the first time on a laptop.
M3 Series (2023–2024)
M3 improved CPU and GPU performance significantly over M2. The Neural Engine improved to ~18 TOPS. Memory bandwidth increased across all tiers.
Key AI specs:
- M3: up to 24 GB unified memory, 100 GB/s bandwidth
- M3 Pro: up to 36 GB unified memory, 150 GB/s bandwidth (slight regression from M2 Pro)
- M3 Max: up to 128 GB unified memory, 400 GB/s bandwidth
- M3 Ultra: up to 192 GB unified memory, 800 GB/s bandwidth
Notable: M3 Pro's memory bandwidth (150 GB/s) is actually lower than M2 Pro (200 GB/s) — a trade-off for power efficiency. For AI workloads, M3 Pro is slower than M2 Pro at the same memory config.
M4 Series (2024–2025)
M4 is the current generation. Major improvements in Neural Engine (38 TOPS — more than double M3) and memory bandwidth.
Key AI specs:
- M4: up to 32 GB unified memory, 120 GB/s bandwidth
- M4 Pro: up to 64 GB unified memory, 273 GB/s bandwidth
- M4 Max: up to 128 GB unified memory, 546 GB/s bandwidth
- M4 Ultra: up to 192 GB unified memory, 820 GB/s bandwidth
Significant: M4's Neural Engine at 38 TOPS enables notably faster MLX inference for models that use it.
Generation Comparison by Tier
Entry Configs (≤32 GB): M2 vs M3 vs M4
| Config | Memory | Bandwidth | ~7B Speed | ~30B Speed |
|---|---|---|---|---|
| M2 (24 GB) | 24 GB | 100 GB/s | ~18 tok/s | Offload needed |
| M3 (24 GB) | 24 GB | 100 GB/s | ~20 tok/s | Offload needed |
| M4 (16/24/32 GB) | Up to 32 GB | 120 GB/s | ~22 tok/s | 30B fits at 32 GB |
| M4 Mac mini (32 GB) | 32 GB | 120 GB/s | ~22 tok/s | Q4 fits, ~6 tok/s |
Verdict at this tier: M4 with 32 GB is the first configuration in the base chip line that comfortably handles 30B models. M2 and M3 at 16–24 GB are limited to 7–14B models. The M4 Mac mini 32 GB ($1,099) is the best entry-level AI Mac available today.
Check which models fit the M4 Mac mini →
Mid-Range (36–64 GB): M2 Pro/Max vs M3 Pro/Max vs M4 Pro
| Config | Memory | Bandwidth | ~30B Speed | ~70B Fits? |
|---|---|---|---|---|
| M2 Pro 32 GB | 32 GB | 200 GB/s | Q4 fits, ~8 tok/s | No (partial offload) |
| M3 Pro 36 GB | 36 GB | 150 GB/s | Q4 comfortable, ~6 tok/s | No |
| M3 Max 48 GB | 48 GB | 300 GB/s | Q6 fits, ~12 tok/s | Q4 tight (~39 GB) |
| M4 Pro 48 GB | 48 GB | 273 GB/s | Q6 fits, ~11 tok/s | Q4 fits (~39 GB) |
| M4 Pro 64 GB | 64 GB | 273 GB/s | Q8 fits, ~11 tok/s | Q5 comfortable |
| M2 Max 64 GB | 64 GB | 400 GB/s | Q8 fits, ~13 tok/s | Q4 comfortable |
| M3 Max 64 GB | 64 GB | 400 GB/s | Q8 fits, ~15 tok/s | Q4 comfortable |
| M4 Max 64 GB | 64 GB | 546 GB/s | Q8 fits, ~19 tok/s | Q4 comfortable |
Notable: M3 Pro has lower bandwidth than M2 Pro, making it a poor choice for AI inference relative to its generation. If buying an M3-series machine for AI, skip the Pro and go for M3 Max.
The 64 GB tier is the first sweet spot for 70B models. M2 Max, M3 Max, and M4 Max at 64 GB all run 70B at Q4 natively. M4 Max is meaningfully faster (~40–50% more bandwidth than M3 Max).
Check which models fit the MacBook Pro M4 Max →
High-End (96–128 GB): M2 Max vs M3 Max vs M4 Max
| Config | Memory | Bandwidth | ~70B Speed | ~100B Fits? |
|---|---|---|---|---|
| M2 Max 96 GB | 96 GB | 400 GB/s | Q6 comfortable, ~10 tok/s | Q3–Q4 fits |
| M3 Max 128 GB | 128 GB | 400 GB/s | Q8 fits, ~12 tok/s | Q4 comfortable |
| M4 Max 128 GB | 128 GB | 546 GB/s | Q8 fits, ~16 tok/s | Q4 comfortable |
At 128 GB, you can run 70B models at Q8 (near-lossless quality) and 100B MoE models at Q4. This is the tier where Apple Silicon is unambiguously the best consumer option — no discrete GPU stack matches it.
Check which models fit the Mac Studio M4 Max →
Extreme: M2 Ultra vs M3 Ultra vs M4 Ultra (192 GB)
| Config | Memory | Bandwidth | ~70B Q8 | ~200B Q4 |
|---|---|---|---|---|
| M2 Ultra 192 GB | 192 GB | 800 GB/s | Comfortable | Fits |
| M3 Ultra 192 GB | 192 GB | 800 GB/s | Comfortable | Fits |
| M4 Ultra 192 GB | 192 GB | 820 GB/s | Fast | Fits comfortably |
All Ultra configurations run 70B at Q8 and can fit models up to ~200B at Q4. The difference between M2 Ultra and M4 Ultra is primarily speed, not capability. M4 Ultra achieves around 20–25% higher throughput on equivalent models due to improved architecture.
At this tier, you can run Llama 3.1 405B with heavy quantization (Q2–Q3) or in FP16 with multi-model inference.
What Models Can Each Tier Run?
Mac mini M4 (16 GB) — $599
- 7B models at Q4–Q6: comfortable
- 14B at Q4: fits but tight
- Recommended: Llama 3.1 8B Q5, Phi-4-mini Q8
Mac mini M4 (32 GB) — $1,099
- 7B at Q8: excellent
- 14B at Q6: comfortable
- 30B at Q4: fits, ~5–6 tok/s
- Recommended: Qwen 2.5 14B Q6, Qwen 3 30B Q4
MacBook Pro M4 Pro (48 GB) — ~$2,499
- 30B at Q6: comfortable
- 70B at Q4: just fits (~39 GB), some headroom
- Recommended: Qwen 3 30B Q6, Llama 3.3 70B Q4
MacBook Pro M4 Max (64 GB) — ~$3,499
- 70B at Q4: comfortable
- 70B at Q6: fits
- Recommended: Llama 3.3 70B Q5, Qwen 2.5 72B Q4
Mac Studio M4 Max (128 GB) — ~$3,999
- 70B at Q8: near-lossless quality
- 100B+ at Q4: fits comfortably
- Recommended: Llama 3.3 70B Q8, Qwen 3 235B-A22B Q4
Mac Studio / Mac Pro M4 Ultra (192 GB) — $5,999+
- 70B at FP16: theoretically fits
- 200B+ at Q4: runs
- DeepSeek R1 671B at Q2: possible
- Recommended for: Llama 3.1 405B Q4, large MoE models at high quality
Speed Comparison: Apple Silicon vs NVIDIA
Apple Silicon consistently underperforms discrete NVIDIA GPUs in tokens-per-second for equivalent models. Here's a rough comparison for Llama 3.3 70B Q4:
| Hardware | ~tok/s (70B Q4) | Notes |
|---|---|---|
| RTX 4090 (24 GB) | 8–12 | With ~30% CPU offload |
| RTX 5090 (32 GB) | 12–16 | Minimal offload |
| M4 Max 64 GB | 8–12 | Fully in unified memory |
| M4 Max 128 GB | 10–14 | More headroom |
| M4 Ultra 192 GB | 18–24 | Full speed, no pressure |
| RTX 4090 (if 70B fit) | ~25–30 | Not achievable — doesn't fit |
For models that fit in both (7B–30B), NVIDIA GPUs are 2–3x faster than Apple Silicon at equivalent memory configurations. For models that only fit on Apple Silicon (70B+ native), it's the only game in town for consumer hardware.
Which Should You Buy?
Already have a Mac: Use what you have. M2 and M3 Macs are capable for AI today. Upgrade when the performance gap starts to matter for your specific workflow.
Buying new primarily for AI: M4 Max 64 GB (MacBook Pro or Mac Studio) is the sweet spot. It runs everything up to 70B natively and everything else at high quality. The M4 Ultra is for power users who need 70B at Q8 or larger MoE models.
Budget-conscious Mac AI setup: M4 Mac mini 32 GB at $1,099. It runs 30B models at Q4 and handles the 14B tier excellently.
Need maximum model capacity anywhere: M4 Max MacBook Pro 128 GB. 128 GB in a laptop is unmatched.
Find what your Mac can run → | Check compatibility for a specific Mac →
Check your hardware compatibility →
Related: M4 Max deep dive | How much VRAM do you need for LLMs? | Best GPU for home AI (NVIDIA)