Apple

Apple M3 Pro (18GB Unified)

Name: Apple M3 Pro (18GB Unified)
Brand: Apple
Price: 1799 USD
Availability: InStock

18 GB · 150 GB/s

$1,799

Updated 2026-03-15

The Apple M3 Pro (18GB Unified) with 18 GB unified memory can handle 42 AI models across chat, coding, ai_coding. Best performance: all-MiniLM-L6-v2 at 1200 tok/s (good). For AI coding workflows, it supports the Capable AI Coding tier — handles single model workflows well. Current price: approximately $1,799.

— OwnRig methodology, data updated 2026-03-15

Builder Capability: Capable AI Coding

Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.

Specifications

Memory

Capacity: 18 GB
Bandwidth: 150 GB/s
Type: Unified Memory

Power & Platform

TDP: 30W
GPU Cores: 14
Host Devices: MacBook Pro 14", MacBook Pro 16"

Compatible Models

Model	Quant	Speed	Rating	Notes
Llama 3.2 1B Instruct	Q8_0	45 tok/s	Good	150 GB/s bandwidth limits throughput vs M4 Pro. Good for lightweight tasks.
Llama 3.2 3B Instruct	Q8_0	35 tok/s	Good	Slower than M4 Pro due to half the bandwidth. Usable for light workloads.
Phi-4 Mini	Q8_0	30 tok/s	Good	Bandwidth-limited. Good for draft model or light coding.
Phi-3 Mini 3.8B Instruct	Q8_0	32 tok/s	Good	Slower than M4 Pro. Acceptable for everyday use.
nomic-embed-text v1.5	Q8_0	600 tok/s	Good	Embedding throughput limited by 150 GB/s. Good for RAG pipelines.
all-MiniLM-L6-v2	FP16	1200 tok/s	Good	Lightweight embeddings. Good for concurrent use with coding models.
Whisper Large V3	Q5_K_M	—	Good	Transcription model. Slower than M4 Pro but usable for voice-to-code.
Whisper Large V3 Turbo	FP16	—	Good	Real-time transcription. 8x faster than full Whisper, minimal quality loss.
Llama 3.1 8B Instruct	Q4_K_M	15 tok/s	Acceptable	150 GB/s is the bottleneck. Usable but slower than M4 Pro.
Mistral 7B Instruct v0.3	Q4_K_M	14 tok/s	Acceptable	Bandwidth-limited. Acceptable for chat and light coding.
Qwen 2.5 7B Instruct	Q4_K_M	16 tok/s	Acceptable	Usable. 150 GB/s limits speed vs discrete GPUs.
Gemma 2 9B Instruct	Q4_K_M	13 tok/s	Acceptable	Slower than M4 Pro. Usable for chat and light coding.
InternLM 2.5 7B Chat	Q4_K_M	15 tok/s	Acceptable	Bandwidth-limited. Acceptable for multilingual chat.
DeepSeek R1 Distill Qwen 7B	Q4_K_M	14 tok/s	Acceptable	Reasoning model. Slower on M3 Pro but usable.
Qwen 2.5 Coder 7B Instruct	Q4_K_M	15 tok/s	Acceptable	Coding model. Usable for Cursor/Continue. Slower than M4 Pro.
Gemma 3 4B	Q4_K_M	22 tok/s	Acceptable	Compact model. Good for resource-constrained coding.
Gemma 3 12B	Q3_K_M	5 tok/s	Marginal	~6.5GB fits in 14GB effective. Very slow at 150 GB/s. Marginal usability.
Phi-3 Medium 14B Instruct	Q3_K_M	6 tok/s	Marginal	Fits at Q3. Very slow at 150 GB/s. Marginal for reasoning tasks.
Phi-4 14B	Q3_K_M	5 tok/s	Marginal	Fits at Q3. Very slow. Marginal for chat and coding.
Qwen 2.5 14B Instruct	Q3_K_M	5 tok/s	Marginal	Fits at Q3. Very slow at 150 GB/s. Marginal.
StarCoder 2 15B	Q3_K_M	4 tok/s	Marginal	Fits at Q3. Very slow. Marginal for code completion.
DeepSeek Coder V2 Lite 16B	Q4_K_M	20 tok/s	Good	MoE 9.1GB fits in 14GB effective. Good coding performance despite low bandwidth.
LLaVA 1.6 13B	Q4_K_M	8 tok/s	Acceptable	Q4_K_M ~8GB fits. Slower at 150 GB/s but usable for image analysis.
Stable Diffusion XL 1.0	FP16	—	Good	5GB fits easily. Slower than M4 Pro. Good for image generation.
Stable Diffusion 3.5 Large	FP16	—	Marginal	FP16 12.5GB fits barely in 14GB effective. Marginal headroom.
Stable Diffusion 3 Medium	FP16	—	Good	5GB fits. Good for SD3 medium quality images.
FLUX.1 Dev	Q4_K_M	—	Not Viable	12B model. Q4 7.2GB fits but 14GB effective + image overhead. Not viable.
Gemma 2 27B Instruct	Q4_K_M	—	Not Viable	Q4 16.3GB exceeds 14GB effective. Doesn't fit.
Gemma 3 27B	Q3_K_M	3 tok/s	Marginal	Q3 13.3GB barely fits in 14GB effective. 3 tok/s. Marginal usability.
Codestral 22B	Q3_K_M	—	Not Viable	Q3 10.3GB + context overhead exceeds 14GB effective. Doesn't fit.
Mistral Small 24B Instruct	Q3_K_M	—	Not Viable	24B exceeds 14GB effective. Doesn't fit.
Yi 1.5 34B Chat	Q3_K_M	—	Not Viable	34B exceeds 14GB effective. Doesn't fit.
Code Llama 34B Instruct	Q3_K_M	—	Not Viable	34B exceeds 14GB effective. Doesn't fit.
Command R 35B	Q3_K_M	—	Not Viable	35B exceeds 14GB effective. Doesn't fit.
QwQ 32B Preview	Q3_K_M	—	Not Viable	32B exceeds 14GB effective. Doesn't fit.
Qwen 2.5 Coder 32B Instruct	Q3_K_M	—	Not Viable	32B exceeds 14GB effective. Doesn't fit.
DeepSeek R1 Distill Qwen 32B	Q3_K_M	—	Not Viable	32B exceeds 14GB effective. Doesn't fit.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 18GB unified.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 18GB unified.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	72B doesn't fit in 18GB unified.
Mixtral 8x7B Instruct	Q2_K	—	Not Viable	MoE 16.4GB exceeds 14GB effective. Doesn't fit.
DeepSeek V3	Q2_K	—	Not Viable	671B MoE. Doesn't fit. Requires 128GB+ Apple Silicon.

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Frequently Asked Questions

What AI models can Apple M3 Pro (18GB Unified) run?: The Apple M3 Pro (18GB Unified) can run 42 AI models. Top performers include all-MiniLM-L6-v2, nomic-embed-text v1.5, Llama 3.2 1B Instruct. See the full compatibility table above for speeds and quality ratings.
Is Apple M3 Pro (18GB Unified) good for AI coding?: Yes. With 18 GB, the Apple M3 Pro (18GB Unified) handles single-model coding workflows well at the Capable tier.
How much VRAM does Apple M3 Pro (18GB Unified) have?: The Apple M3 Pro (18GB Unified) has 18 GB of unified memory with 150 GB/s bandwidth.
Can Apple M3 Pro (18GB Unified) run 70B models?: 70B models can run on the Apple M3 Pro (18GB Unified) with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
Is Apple M3 Pro (18GB Unified) worth it for AI?: At $1,799, the Apple M3 Pro (18GB Unified) offers 18 GB VRAM and runs 42 AI models. It's a good entry point for smaller models and experimentation.

Generation: M3. Last updated: 2026-03-15.

Compatible Models

Model	Quant	Speed	Rating	Notes
Llama 3.2 1B Instruct	Q8_0	45 tok/s	Good	150 GB/s bandwidth limits throughput vs M4 Pro. Good for lightweight tasks.
Llama 3.2 3B Instruct	Q8_0	35 tok/s	Good	Slower than M4 Pro due to half the bandwidth. Usable for light workloads.
Phi-4 Mini	Q8_0	30 tok/s	Good	Bandwidth-limited. Good for draft model or light coding.
Phi-3 Mini 3.8B Instruct	Q8_0	32 tok/s	Good	Slower than M4 Pro. Acceptable for everyday use.
nomic-embed-text v1.5	Q8_0	600 tok/s	Good	Embedding throughput limited by 150 GB/s. Good for RAG pipelines.
all-MiniLM-L6-v2	FP16	1200 tok/s	Good	Lightweight embeddings. Good for concurrent use with coding models.
Whisper Large V3	Q5_K_M	—	Good	Transcription model. Slower than M4 Pro but usable for voice-to-code.
Whisper Large V3 Turbo	FP16	—	Good	Real-time transcription. 8x faster than full Whisper, minimal quality loss.
Llama 3.1 8B Instruct	Q4_K_M	15 tok/s	Acceptable	150 GB/s is the bottleneck. Usable but slower than M4 Pro.
Mistral 7B Instruct v0.3	Q4_K_M	14 tok/s	Acceptable	Bandwidth-limited. Acceptable for chat and light coding.
Qwen 2.5 7B Instruct	Q4_K_M	16 tok/s	Acceptable	Usable. 150 GB/s limits speed vs discrete GPUs.
Gemma 2 9B Instruct	Q4_K_M	13 tok/s	Acceptable	Slower than M4 Pro. Usable for chat and light coding.
InternLM 2.5 7B Chat	Q4_K_M	15 tok/s	Acceptable	Bandwidth-limited. Acceptable for multilingual chat.
DeepSeek R1 Distill Qwen 7B	Q4_K_M	14 tok/s	Acceptable	Reasoning model. Slower on M3 Pro but usable.
Qwen 2.5 Coder 7B Instruct	Q4_K_M	15 tok/s	Acceptable	Coding model. Usable for Cursor/Continue. Slower than M4 Pro.
Gemma 3 4B	Q4_K_M	22 tok/s	Acceptable	Compact model. Good for resource-constrained coding.
Gemma 3 12B	Q3_K_M	5 tok/s	Marginal	~6.5GB fits in 14GB effective. Very slow at 150 GB/s. Marginal usability.
Phi-3 Medium 14B Instruct	Q3_K_M	6 tok/s	Marginal	Fits at Q3. Very slow at 150 GB/s. Marginal for reasoning tasks.
Phi-4 14B	Q3_K_M	5 tok/s	Marginal	Fits at Q3. Very slow. Marginal for chat and coding.
Qwen 2.5 14B Instruct	Q3_K_M	5 tok/s	Marginal	Fits at Q3. Very slow at 150 GB/s. Marginal.
StarCoder 2 15B	Q3_K_M	4 tok/s	Marginal	Fits at Q3. Very slow. Marginal for code completion.
DeepSeek Coder V2 Lite 16B	Q4_K_M	20 tok/s	Good	MoE 9.1GB fits in 14GB effective. Good coding performance despite low bandwidth.
LLaVA 1.6 13B	Q4_K_M	8 tok/s	Acceptable	Q4_K_M ~8GB fits. Slower at 150 GB/s but usable for image analysis.
Stable Diffusion XL 1.0	FP16	—	Good	5GB fits easily. Slower than M4 Pro. Good for image generation.
Stable Diffusion 3.5 Large	FP16	—	Marginal	FP16 12.5GB fits barely in 14GB effective. Marginal headroom.
Stable Diffusion 3 Medium	FP16	—	Good	5GB fits. Good for SD3 medium quality images.
FLUX.1 Dev	Q4_K_M	—	Not Viable	12B model. Q4 7.2GB fits but 14GB effective + image overhead. Not viable.
Gemma 2 27B Instruct	Q4_K_M	—	Not Viable	Q4 16.3GB exceeds 14GB effective. Doesn't fit.
Gemma 3 27B	Q3_K_M	3 tok/s	Marginal	Q3 13.3GB barely fits in 14GB effective. 3 tok/s. Marginal usability.
Codestral 22B	Q3_K_M	—	Not Viable	Q3 10.3GB + context overhead exceeds 14GB effective. Doesn't fit.
Mistral Small 24B Instruct	Q3_K_M	—	Not Viable	24B exceeds 14GB effective. Doesn't fit.
Yi 1.5 34B Chat	Q3_K_M	—	Not Viable	34B exceeds 14GB effective. Doesn't fit.
Code Llama 34B Instruct	Q3_K_M	—	Not Viable	34B exceeds 14GB effective. Doesn't fit.
Command R 35B	Q3_K_M	—	Not Viable	35B exceeds 14GB effective. Doesn't fit.
QwQ 32B Preview	Q3_K_M	—	Not Viable	32B exceeds 14GB effective. Doesn't fit.
Qwen 2.5 Coder 32B Instruct	Q3_K_M	—	Not Viable	32B exceeds 14GB effective. Doesn't fit.
DeepSeek R1 Distill Qwen 32B	Q3_K_M	—	Not Viable	32B exceeds 14GB effective. Doesn't fit.
Llama 3.1 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 18GB unified.
Llama 3.3 70B Instruct	Q2_K	—	Not Viable	70B doesn't fit in 18GB unified.
Qwen 2.5 72B Instruct	Q2_K	—	Not Viable	72B doesn't fit in 18GB unified.
Mixtral 8x7B Instruct	Q2_K	—	Not Viable	MoE 16.4GB exceeds 14GB effective. Doesn't fit.
DeepSeek V3	Q2_K	—	Not Viable	671B MoE. Doesn't fit. Requires 128GB+ Apple Silicon.

Frequently Asked Questions

What AI models can Apple M3 Pro (18GB Unified) run?

The Apple M3 Pro (18GB Unified) can run 42 AI models. Top performers include all-MiniLM-L6-v2, nomic-embed-text v1.5, Llama 3.2 1B Instruct. See the full compatibility table above for speeds and quality ratings.

Is Apple M3 Pro (18GB Unified) good for AI coding?

Yes. With 18 GB, the Apple M3 Pro (18GB Unified) handles single-model coding workflows well at the Capable tier.

How much VRAM does Apple M3 Pro (18GB Unified) have?

The Apple M3 Pro (18GB Unified) has 18 GB of unified memory with 150 GB/s bandwidth.

Can Apple M3 Pro (18GB Unified) run 70B models?

70B models can run on the Apple M3 Pro (18GB Unified) with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.

Is Apple M3 Pro (18GB Unified) worth it for AI?

At $1,799, the Apple M3 Pro (18GB Unified) offers 18 GB VRAM and runs 42 AI models. It's a good entry point for smaller models and experimentation.