Apple M2 Pro
The Apple M2 Pro ships in 16–32 GB unified-memory configurations at 200 GB/s. Across those configurations it runs 46 of our 84 tracked models natively in VRAM at 8k context.
More memory means more of our tracked models fit natively — see which configuration you need below.
| Configuration | Bandwidth | CPU cores | Native models | + Offload |
|---|---|---|---|---|
| 32 GB | 200 GB/s | 12 (8P + 4E) | 46 / 84 | 0 |
| 16 GB | 200 GB/s | 12 (8P + 4E) | 26 / 84 | 0 |
How much of the Apple M2 Pro's memory is actually usable?
macOS and background apps need a slice of the pool before a model gets to use it — this site reserves 8GB on every unified-memory GPU, the same baseline used everywhere else on this site. What's left is real headroom for a model's weights and KV cache:
M2 Pro's bandwidth was never beaten by its immediate successor
Apple's Pro-tier bandwidth doesn't climb every generation. Here's the top memory bandwidth for each generation's Pro-tier chip, from the original M1 Pro through the M4 Pro:
M1 Pro and M2 Pro (this page) both shipped at exactly 200 GB/s a year apart — no change. The M3 Pro that followed actually cut it to 150 GB/s, a 25% regression, before the M4 Pro recovered to 273 GB/s and pushed past what this chip already had in 2023. Since LLM decode is bandwidth-bound, a used M2 Pro can out-decode a same-capacity M3 Pro on an identical model — worth knowing if you're shopping the used market by generation number rather than by spec sheet.
Apple M2 Pro (32GB)
With 32 GB LPDDR5 at 200 GB/s, this configuration runs 46 models natively. It comfortably runs 7B–32B models at Q4; 70B-class models typically need CPU offload.
Apple M2 Pro (32GB): the double-memory configuration of Apple's second-generation Pro chip, launched January 2023 as a build-to-order upgrade on the 14-inch and 16-inch MacBook Pro and, later that year, the Mac mini. Same 200 GB/s LPDDR5 bandwidth and 12-core CPU (8P+4E) as the 16GB sibling — only the memory pool triples the sibling's usable headroom. Worth naming: M2 Pro's 200 GB/s was never beaten by its immediate successor. The M3 Pro that replaced it a year later actually cut bandwidth to 150 GB/s, and it took until the M4 Pro (273 GB/s) for Apple's Pro tier to move past what this chip already had in 2023.
24 GB of real headroom after the standard 8 GB reservation lets this site's calculator fit Qwen3 32B natively at Q4_K_M (23.9 GB, ~7.5 tok/s) and Mixtral 8x7B at Q2_K (~9.2 tok/s), both out of reach on the 16GB sibling. Mid-size models get precision headroom too: Llama 3.1 8B and Qwen2.5 7B fit at full BF16 instead of Q5/Q6, at roughly 9.4-10.2 tok/s. 46 of the 84 tracked models fit natively, up from 26 on the 16GB sibling — the extra 16 GB unlocks a full tier of 32-35B models, not just more headroom on the same 7-14B models.
Full MLX and llama.cpp Metal support, same as the 16GB sibling. Community llama.cpp benchmarks on 19-core-GPU M2 Pro report roughly 38 tok/s decode on a 7B model at Q4_0 (ggml-org/llama.cpp Discussion #4167), well ahead of the base M2's ~22 tok/s at the same quant — consistent with the 200 vs 100 GB/s bandwidth difference, though real-world gains from doubled bandwidth often fall short of a clean 2x due to compute-side overhead at small batch sizes.
Models the 32 GB configuration runs natively (46)
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q2_K · ~9.2 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q3_K_M · ~32.1 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q3_K_M · ~8.4 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q3_K_M · ~8.6 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q4_K_M · ~7.5 t/s
Show 41 more
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q3_K_M · ~9 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4Q3_K_M · ~9 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q3_K_M · ~9 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q4_K_M · ~24.5 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2Q3_K_M · ~8.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q4_K_M · ~23.2 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q4_K_M · ~8.1 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q5_K_M · ~7.7 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q5_K_M · ~8.1 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~18.2 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6Q5_K_M · ~17 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q6_K · ~7.6 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q6_K · ~8 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q6_K · ~15.9 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q8_0 · ~9.4 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q8_0 · ~9.3 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q8_0 · ~9.9 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q8_0 · ~11.2 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q8_0 · ~11.4 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q8_0 · ~10 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~7.5 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~8.8 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~9.4 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~9.4 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~9.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~10.2 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~10.3 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~9.7 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~9.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~8.7 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3FP32 · ~9.8 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~11.6 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~12.6 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~14.2 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~19 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~19 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~25.7 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~30.6 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~37 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~76.2 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~90.1 t/s
Apple M2 Pro (16GB)
With 16 GB LPDDR5 at 200 GB/s, this configuration runs 26 models natively. It handles smaller models (7B–14B) at Q4–Q5 quantization.
Apple M2 Pro (16GB): the base memory tier of Apple's second-generation Pro chip, standard on the $1,999 14-inch MacBook Pro starting January 2023. The cheapest configuration pairs a binned 10-core CPU (6P+4E) with a 16-core GPU; every other M2 Pro machine — the 16-inch MacBook Pro, the Mac mini, and the 32GB configuration tracked separately on this site (m2-pro-32) — gets the full 12-core CPU (8P+4E) and 19-core GPU. Both binnings share the identical 200 GB/s LPDDR5 bandwidth.
This site's calculator reserves 8 GB of the 16 GB pool for macOS and background apps — leaving 8 GB of real headroom, a third of the 32GB sibling's 24 GB from the identical reservation. That caps sensible model choices around 7-9B parameters at real quality: Llama 3.1 8B fits at Q5_K_M (7.58 GB, ~23.6 tok/s) and Qwen2.5 7B fits at Q6_K (7.51 GB, ~23.8 tok/s) — both roughly double the identical-capacity base M2 16GB's speed, since 200 GB/s is twice the base M2's 100 GB/s. Reach for 12-14B models and the pool only has room at the bottom of the quant ladder: Qwen3 14B needs Q2_K (7.82 GB). 26 of the 84 tracked models fit natively.
macOS's default Metal working-set limit (roughly 75% of unified memory) is about 12 GB on this configuration. Community guides for raising it with `sudo sysctl iogpu.wired_limit_mb=...` generally recommend leaving 8-16 GB in reserve for macOS itself — on a 16 GB machine that reserve is most of the pool, so there's little safe room to push past what this page already assumes.
Models the 16 GB configuration runs natively (26)
- Bonsai 27B27B · MMLU-Pro 81.51-bit (Q1_0) · ~29.6 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q2_K · ~22.9 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q2_K · ~24 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q2_K · ~26.7 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q3_K_M · ~23.1 t/s
Show 21 more
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0Q2_K · ~25.3 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5Q5_K_M · ~24 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3Q5_K_M · ~23.6 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0Q5_K_M · ~23.6 t/s
- Qwen3 8B8B · MMLU-Pro 56.7Q5_K_M · ~23.2 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3Q6_K · ~23.8 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0Q6_K · ~22.8 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6Q8_0 · ~33.6 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4Q8_0 · ~30.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4Q6_K · ~25.2 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3Q8_0 · ~31.3 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0Q8_0 · ~36.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~24.6 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~26.3 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~36.3 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~31.9 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~25.7 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~30.6 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~37 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~76.2 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~90.1 t/s
Too large for any Apple M2 Pro configuration (38)
- Llama 3.3 70B Instruct
- Qwen 2.5 72B Instruct
- DeepSeek R1 Distill Llama 70B
- Command-R 35B
- Llama 3.1 70B Instruct
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- Mistral Medium 3.5 128B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
Continue reading
Frequently asked questions
- How much memory does the Apple M2 Pro have?
- The Apple M2 Pro ships in 2 unified-memory configurations: 32 GB and 16 GB, all at 200 GB/s.
- Should I get the 16 GB or 32 GB Apple M2 Pro?
- Both run everything that fits natively in 16 GB. The extra memory in the 32 GB configuration additionally fits Mixtral 8x7B Instruct v0.1, Qwen 3.5 35B-A3B (MoE), Qwen 3.6 35B, and 17 more models natively in VRAM — worth the upgrade if you plan to run any of those.
- How much VRAM does the Apple M2 Pro (32GB) have?
- The Apple M2 Pro (32GB) has 32 GB of LPDDR5 with 200 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M2 Pro (32GB) best for?
- With 32 GB of VRAM, the Apple M2 Pro (32GB) is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the Apple M2 Pro (32GB) run locally?
- The Apple M2 Pro (32GB) can run 46 of the 84 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at BF16, Llama 3.2 3B Instruct at FP32, Llama 3.2 1B Instruct at FP32.
- Can the Apple M2 Pro (32GB) run Llama 3.3 70B Instruct?
- The Apple M2 Pro (32GB) does not have enough VRAM to run Llama 3.3 70B Instruct. You would need more VRAM or a lower quantization level.
Show 8 more questions
- Can the Apple M2 Pro (32GB) run Qwen 3.6 27B?
- Yes. The Apple M2 Pro (32GB) runs Qwen 3.6 27B natively in VRAM at Q5_K_M quantization, achieving approximately 8.1 tokens per second.
- Can the Apple M2 Pro (32GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M2 Pro (32GB) runs Llama 3.1 8B Instruct natively in VRAM at BF16 quantization, achieving approximately 9.4 tokens per second.
- How much VRAM does the Apple M2 Pro (16GB) have?
- The Apple M2 Pro (16GB) has 16 GB of LPDDR5 with 200 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M2 Pro (16GB) best for?
- With 16 GB of VRAM, the Apple M2 Pro (16GB) handles smaller models (7B–14B) at Q4–Q5 quantization — ideal for entry-level local LLM experimentation and lightweight inference.
- What LLMs can the Apple M2 Pro (16GB) run locally?
- The Apple M2 Pro (16GB) can run 26 of the 84 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at Q5_K_M, Llama 3.2 3B Instruct at Q8_0, Llama 3.2 1B Instruct at FP32.
- Can the Apple M2 Pro (16GB) run Llama 3.3 70B Instruct?
- The Apple M2 Pro (16GB) does not have enough VRAM to run Llama 3.3 70B Instruct. You would need more VRAM or a lower quantization level.
- Can the Apple M2 Pro (16GB) run Qwen 3.6 27B?
- The Apple M2 Pro (16GB) does not have enough VRAM to run Qwen 3.6 27B. You would need more VRAM or a lower quantization level.
- Can the Apple M2 Pro (16GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M2 Pro (16GB) runs Llama 3.1 8B Instruct natively in VRAM at Q5_K_M quantization, achieving approximately 23.6 tokens per second.