Apple MacBook Pro (14-inch, 2023) (M2 Pro or M2 Max)

Hardware · Apple Silicon

What AI models can the Apple M2 Pro (16 GB) run?

Unified memory
16.0 GB
Usable for models
10.7 GB
Memory bandwidth
200 GB/s
FP16 compute
6.8 TFLOPS
Released
2023
380 / 622 LLMs run Best: K2 Horizon 7B 53 / 61 Image models run Best: FLUX.2 [klein] 4B 12 / 43 Video models run Best: LTX-Video 2B 64 / 70 Decision models run Best: Jina Reranker v3.5

macOS lets the GPU use roughly two-thirds to three-quarters of unified memory by default, so we plan around that figure rather than the full 16.0 GB.

More about this hardware

MakerApple Silicon
TypeApple silicon (system on a chip)
ChipApple M2 Pro

Apps that run models well on the Apple M2 Pro (16 GB)

macOS

  1. LM StudioRuns the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon.
  2. OllamaOne command with Ollama’s own build of this model.
  3. MLX LMRuns the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon.
  4. JanRuns the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon.

For a typical model that fits in memory. Each model page picks the best app for that model on this hardware. All apps

3 products with the Apple M2 Pro (16 GB)

Best LLMs for the Apple M2 Pro (16 GB)

Quality Model Quant Memory Speed Runs
76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7BInstitute of Foundation Models Q4_K_M 8.2 GB 23.5 tok/s Runs well
67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Nemotron 3.5 Bolt E4BNVIDIA Q5_K_M 7.3 GB 38.8 tok/s Runs great
66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. Mellum2.1 Thinking (12B-A2.5B)JetBrains Q4_K_M 9.0 GB 65.8 tok/s Runs great
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M 7.5 GB 22.5 tok/s Runs well
65Good quality (B) · 65 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E4BGoogle Q5_K_M 7.3 GB 38.8 tok/s Runs great
78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 12BGoogle Q4_K_M 9.4 GB 17.7 tok/s Runs welltight fit
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B PreviewSenseTime Q4_K_M 7.8 GB 21.9 tok/s Runs well
73Great quality (A) · 73 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 9BDeepReinforce Q4_K_M 7.6 GB 21.9 tok/s Runs well
72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 9BAlibaba Q4_K_M 7.6 GB 21.9 tok/s Runs well
71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 9BDeepReinforce Q4_K_M 7.6 GB 21.9 tok/s Runs well
70Great quality (A) · 70 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. TMax 9BAi2 Q4_K_M 7.2 GB 23.6 tok/s Runs well
62Good quality (B) · 62 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E2BGoogle Q8_0 7.0 GB 46.8 tok/s Runs great

Best Image models for the Apple M2 Pro (16 GB)

Quality Model Quant Memory Speed Runs
76Great quality (A) · 76 of 100: how capable the model is at FP8, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP8 10.2 GB 11 s Runs welltight fit
84Great quality (A) · 84 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q4 GGUF 10.2 GB 85 s Slowtight fit
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) Q4 GGUF 10.1 GB 37 s Runs welltight fit
54Fair quality (C) · 54 of 100: how capable the model is at FP16, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. SDXL-Lightning (4-step)ByteDance FP16 8.9 GB 8.7 s Runs great
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 9BBlack Forest Labs Q4 GGUF 8.0 GB 28 s Runs wellencoder unloaded after use
79Great quality (A) · 79 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-Image TurboBaidu Q4 GGUF 7.4 GB 50 s Slowencoder unloaded after use

Best Video models for the Apple M2 Pro (16 GB)

Quality Model Quant Memory Speed Runs
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 6.1 GB 4.9 min Runs wellencoder unloaded after use
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5)Shanghai AI Lab FP16 4.7 GB 4.1 min Runs well
55Good quality (B) · 55 of 100: how capable the model is at FP16, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3BAlibaba FP16 8.4 GB 51.9 min Very slowencoder unloaded after use
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 VACE 1.3BAlibaba FP8 8.0 GB 50.6 min Very slowencoder unloaded after use
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video)Tencent FP8 7.8 GB 2.1 h Very slowCPU offload

Best Decision models for the Apple M2 Pro (16 GB)

Quality Model Quant Memory Speed Runs
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI F16 2.9 GB 89 ms Runs great
84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI F16 2.9 GB 89 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. FRIDA-DecisionsSber AI F16 2.4 GB 66 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino F16 1.7 GB 41 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 0.6BAlibaba F16 2.9 GB 89 ms Runs great
76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Llama Prompt Guard 2 86MMeta F16 1.2 GB 47 ms Runs great
76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-base v2Mixedbread F16 2.5 GB 73 ms Runs great
74Great quality (A) · 74 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. GLiClass ModernBERT Large v3Knowledgator F16 1.5 GB 34 ms Runs great

Best LLMs by task on this hardware Open in Model Finder

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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