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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.

Jump to:32 GB16 GB
ConfigurationBandwidthCPU coresNative models+ Offload
32 GB200 GB/s12 (8P + 4E)46 / 840
16 GB200 GB/s12 (8P + 4E)26 / 840
Vendor: Apple
Memory type: LPDDR5
Compute backend: METAL
Software: MLX gives the best performance on Apple Silicon; llama.cpp Metal backend is a solid alternative. Both are well-supported by Ollama.

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:

macOS + background apps (8 GB reserved)usable for model weights + KV cacheVertical lines mark roughly where a 7B/14B/32B/... dense model at Q4_K_M lands.

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:

Apple M1 Pro (32GB)
200 GB/s
Apple M2 Pro (32GB) — this page
200 GB/s
Apple M3 Pro (36GB)
150 GB/s
Apple M4 Pro (48GB)
273 GB/s

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)

Show 41 more

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)

Show 21 more

Too large for any Apple M2 Pro configuration (38)

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.