Apple M2 (16GB Unified)
16 GB Unified · 100 GB/s
From
$1,099
Estimated street price
VRAM
16 GB
Bandwidth
100 GB/s
TDP
22W
Models
24
Tier
Capable
The Apple M2 (16GB Unified) with 16 GB unified memory can handle 24 AI models across chat, coding, ai_coding. Best performance: Llama 3.2 1B Instruct at 22 tok/s (good). For AI coding workflows, it supports the Capable AI Coding tier, handling single model workflows well. Current price: approximately $1,099.
Source: OwnRig methodology
16 GB
100 GB/s
Unified
22W
10
MacBook Air 13" (2022), MacBook Air 15" (2023), MacBook Pro 13" (2022)
Builder Capability: Capable AI Coding
Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.
Inference Backends
The software stacks that matter most for real-world inference on this device.
Metal
productionPrimary backend for Apple Silicon. ~13–14GB available for models after macOS overhead.
What it can run
24 models| Arcee Trinity Mini 26B | Q3_K_M | – | Not viable |
| Arcee Trinity Nano 6B | Q8_0 | 12 tok/s | Acceptable |
| DeepSeek V3 | Q2_K | – | Not viable |
| Gemma 3 27B | Q4_K_M | – | Not viable |
| Gemma 3 4B | Q5_K_M | 9 tok/s | Acceptable |
| Gemma 4 26B-A4B | Q3_K_M | – | Not viable |
| Gemma 4 31B | Q3_K_M | – | Not viable |
| Gemma 4 E2B | Q8_0 | 9 tok/s | Acceptable |
| Gemma 4 E4B | Q8_0 | 5 tok/s | Marginal |
| GigaChat Lightning 10B | Q8_0 | 22 tok/s | Good |
| Llama 3.1 8B Instruct | Q8_0 | 8 tok/s | Acceptable |
| Llama 3.2 11B Vision | Q8_0 | 7 tok/s | Acceptable |
| Llama 3.2 1B Instruct | Q8_0 | 22 tok/s | Good |
| Llama 3.2 3B Instruct | Q8_0 | 14 tok/s | Good |
| NVIDIA Nemotron-3-super-120B-A12B | Q2_K | – | Not viable |
| Phi-4 Mini | Q8_0 | 14 tok/s | Good |
| Qwen 2.5 Coder 32B Instruct | Q4_K_M | – | Not viable |
| Qwen 2.5 Coder 7B Instruct | Q5_K_M | 9 tok/s | Acceptable |
| Qwen3.5-122B-A10B | Q3_K_M | – | Not viable |
| Qwen3.5-27B | Q3_K_M | 10 tok/s | Acceptable |
| Qwen3.5-397B (MoE) | Q2_K | – | Not viable |
| Qwen3.6-27B | Q3_K_M | 10 tok/s | Acceptable |
| Stable Diffusion 3.5 Large | FP16 | – | Acceptable |
| Whisper Large V3 Turbo | FP16 | – | Good |
Showing 24 of 24 entries
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Frequently Asked Questions
- What AI models can Apple M2 (16GB Unified) run?
- The Apple M2 (16GB Unified) can run 24 AI models. Top performers include Llama 3.2 1B Instruct, GigaChat Lightning 10B, Llama 3.2 3B Instruct. See the full compatibility table above for speeds and quality ratings.
- Is Apple M2 (16GB Unified) good for AI coding?
- Yes. With 16 GB, the Apple M2 (16GB Unified) handles single-model coding workflows well at the Capable tier.
- How much VRAM does Apple M2 (16GB Unified) have?
- The Apple M2 (16GB Unified) has 16 GB of unified memory with 100 GB/s bandwidth.
- Can Apple M2 (16GB Unified) run 70B models?
- 70B models can run on the Apple M2 (16GB Unified) with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
- Is Apple M2 (16GB Unified) worth it for AI?
- At $1,099, the Apple M2 (16GB Unified) offers 16 GB VRAM and runs 24 AI models. It works for smaller models and experimentation.
Own this GPU?
See every AI model it supports, expected performance, and how to build around it.