30.5Bティア100約22.2 GB必要
Apple
MacBook Pro M3 Pro 18GB
M3LaptopM3UNIFIEDMetal
18GB
Unified Memory
150GB/s
Bandwidth
$1,999 MSRP
Operating mode
Choose the run profile you want to optimize
Apple Silicon can fit a lot thanks to unified memory. This selector changes which serving posture we optimize for when surfacing the best local LLMs for this Mac.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Best Local LLMs for MacBook Pro M3 Pro 18GB
Apple Silicon local AI performance. Excellent for local AI. Your MacBook Pro M3 Pro 18GB with 18 GB unified memory can run 59 models natively, 172 more with limits. The best match is Qwen 3.5 9B at 21 tok/s for interactive local LLM use.
59
Run great
231
Total compatible
14.699999809265137B
Max parameters
21
Best tok/sEST.
Comparison guide
Best Local LLMs for MacBook Pro M3 Pro 18GB — full ranked guide
Top models ranked for coding, chat, and writing with FAQ and buyer guidance — the comparison-intent companion to this spec sheet.
Quick picks
Best Local LLMs by Task
Top recommendations for common local AI workloads on your MacBook Pro M3 Pro 18GB
About MacBook Pro M3 Pro 18GB for AI
MacBook Pro M3 Pro 18GB with 18 GB unified memory. Third-generation Apple Silicon built on 3nm process with dynamic caching GPU architecture, significantly improving AI inference efficiency.
All 374 models tested
Model Compatibility Tiers
Every model ranked by how well it runs on your MacBook Pro M3 Pro 18GB, grouped by fit quality
Runs Great (59 models)
These models fit comfortably and run at full speed on your Mac.
Runs with Limits (185 models)
These models run but may need quantization or have reduced context windows.
Won't Fit (130 models)
These models are too large for your Mac's unified memory.
Beyond LLMs
AI Capability Matrix
What AI tasks this Mac can handle — from text generation to image and video creation.
| Capability | Status | Representative Model |
|---|---|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 |
| LLM Large (70B) | Won’t fit | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Very constrained | SD 3.5 Large FP16 |
| Video Short (25f) | Runs with offload | LTX Video 2B |
| Video Long (100f) | Won't fit | Wan Video 14B |
Same chip, more memory
Upgrade to More Memory? Here's What You Gain
Compare M3 configurations to see which models become available
MacBook Pro M3 24GB
24 GB unified memory
76
Run great
257
Total fit
Unlocks: Magistral Small 2507, Devstral Small 2 24B Instruct
MacBook Air M3 24GB
24 GB unified memory
76
Run great
257
Total fit
Unlocks: Magistral Small 2507, Devstral Small 2 24B Instruct
MacBook Pro M3 Pro 36GB
36 GB unified memory
94
Run great
304
Total fit
Unlocks: Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B
limited-memorymlx-optimizedportable
仕様
コンピュート
アーキテクチャM3
メモリ
ユニファイドメモリ18 GB
帯域幅150 GB/s
一般
ファミリーM3
セグメントLaptop
インターコネクトUNIFIED
コンピュートプラットフォームMETAL
MSRP$1,999
主な特徴
M3 chip (3nm TSMC)18 GB unified memory (shared CPU/GPU/Neural Engine)150 GB/s memory bandwidth16-core Neural EngineMetal 3 GPU compute (MLX framework)MacBook Pro 14"/16" form factor
AIワークロード向け
強み
- 3nm process enables higher efficiency
- Dynamic caching GPU improves utilization
- Up to 400 GB/s memory bandwidth (Max)
- Hardware-accelerated ray tracing
- Strong MLX optimization
注意点
- Base M3 still limited to 24 GB unified memory
- Premium pricing for high-memory configurations
Architecture
M3
Apple M3 is built on TSMC's 3nm process, the first consumer chips at this node. It introduces Dynamic Caching for more efficient GPU memory allocation and hardware-accelerated ray tracing.
AI Relevance
Dynamic Caching improves GPU utilization for compute workloads including ML inference. The M3 Ultra with up to 512 GB unified memory can theoretically hold even unquantized 70B models, though memory bandwidth remains the throughput bottleneck.
Process: TSMC 3nmPlatform: METALPrecisions: FP32, FP16
M3's dynamic caching GPU architecture allocates local memory in hardware in real-time, improving GPU utilization for AI workloads. The M3 Max reaches 400 GB/s bandwidth, competitive with mid-range discrete GPUs.
All workloads
Recommendations by Workload
The best local LLM for each task on your MacBook Pro M3 Pro 18GB
Chat
SQwen 3.5 9B
This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
21.4 tok/s · 34K ctx · llama.cpp
9.4 GB / 18.0 GB Unified Memory
Coding
SQwen 3.5 9B
This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
21.4 tok/s · 34K ctx · llama.cpp
10.5 GB / 18.0 GB Unified Memory
Agentic Coding
AGemma 4 E4B
This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
18.3 tok/s · 65K ctx · llama.cpp
10.3 GB / 18.0 GB Unified Memory
Reasoning
SQwen 3.5 9B
This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
21.4 tok/s · 34K ctx · llama.cpp
10.5 GB / 18.0 GB Unified Memory
RAG
ACodeGeeX 4 9B
This model is still usable for rag, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.
21.8 tok/s · 121K ctx · llama.cpp
9.6 GB / 18.0 GB Unified Memory
もう少しで届く
アップグレードで動くモデル
もう少しメモリがあれば動く高品質モデル
397Bティア100約246.4 GB必要
123Bティア100約80.6 GB必要
1000Bティア100約616.6 GB必要
1000Bティア100約616.6 GB必要
Image & Video Generation
Diffusion Model Compatibility
25 of 52 models can generate images or video on your MacBook Pro M3 Pro 18GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~4.3s | S |
| Stable Diffusion 1.5Image | 512×768 | ~8.5s | S |
| Realistic Vision v5.1Image | 512×768 | ~8.5s | S |
| DreamShaper 8Image | 512×768 | ~8.5s | S |
| LCM DreamShaper v7Image | 512×768 | ~2.6s | S |
| PixArt-SigmaImage | 1024×1024 | ~34.1s | S |
| SDXL TurboImage | 512×512 | ~4.3s | S |
| SDXL LightningImage | 1024×1024 | ~12.8s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~34.1s | S |
| Playground v2.5Image | 1024×1024 | ~51.2s | S |
| RealVisXL v5.0Image | 1024×1024 | ~38.4s | S |
| DreamShaper XLImage | 1024×1024 | ~38.4s | S |
| Juggernaut XL v9Image | 1024×1024 | ~38.4s | S |
| Animagine XL 3.1Image | 1024×1024 | ~38.4s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~38.4s | S |
| Animagine XL 4.0Image | 1024×1024 | ~38.4s | S |
| Illustrious XLImage | 1024×1024 | ~38.4s | S |
| Stable Diffusion 3.5 MediumImage | 256×256 | ~2m 59s | S |
| Flux.2 Klein 4BImage | 256×256 | ~23s | S |
| LTX Video 2BVideo | 256×256 | ~29.6s/frame | B |
| KolorsImage | 256×256 | ~1m 8s | B |
| Stable CascadeImage | 1024×1024 | ~1m 25s | D |
| AuraFlow v0.3Image | 256×256 | ~2m 34s | D |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~3m 8s | D |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~34.1s | D |
| FramePack I2VVideo | 256×256 | ~1m 3s/frame | F |
| Wan Video 2.1 1.3BVideo | 256×256 | ~25s/frame | F |
| CogVideoX 2BVideo | 256×256 | ~29.6s/frame | F |
| HunyuanVideoVideo | 256×256 | ~1m 3s/frame | F |
| ChromaImage | 256×256 | ~34.1s | F |
| Z-Image TurboImage | 256×256 | ~35.2s | F |
| Flux.1 DevImage | 256×256 | ~2m 34s | F |
| Flux.1 SchnellImage | 256×256 | ~29.9s | F |
| LTX Video 13BVideo | 256×256 | ~1m 3s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~2m 51s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~15.6s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~48.9s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~42.8s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~42.8s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~17.1s | F |
| Flux.1 Fill DevImage | 256×256 | ~2m 25s | F |
| Mochi 1 PreviewVideo | 256×256 | ~56.4s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~52.4s/frame | F |
| Helios 14BVideo | 256×256 | ~1m 5s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~1m 5s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~1m 5s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~1m 5s/frame | F |
| Qwen ImageImage | 256×256 | ~57.5s | F |
| Qwen Image EditImage | 256×256 | ~57.5s | F |
| Flux.2 DevImage | 256×256 | ~26m 56s | F |
| MAGI-1Video | 256×256 | ~1m 20s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 41s | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Get started in 2 minutes
Run Local AI on Your MacBook Pro M3 Pro 18GB
Everything you need to start running models locally with Metal acceleration and Apple Silicon unified memory
1
Install Ollama
Ollama runs natively on macOS with Metal GPU acceleration. One command to install.
curl -fsSL https://ollama.com/install.sh | sh2
Pull your first model
Qwen 3.5 9B is the best match for your MacBook Pro M3 Pro 18GB. Pull and run it:
ollama run qwen3.5:9bWhat to expect: With 18 GB unified memory, your top models will run at 21-24-48 tokens/sec — fast enough for interactive chat and local LLM workflows. Cloud APIs like ChatGPT typically stream at 30-60 tok/s, so Apple Silicon is competitive for many models when the fit is good.
Upgrade paths
Upgrade from MacBook Pro M3 Pro 18GB
See what you unlock with more unified memory
アップグレードオプション
アップグレードオプション
RTX 4060 Ti 16GB次のステップ288 GB/s (+138)
A
Unlocks 36 additional models that do not fit on the current setup.解放されるモデル Magistral Small 2507, Devstral Small 2 24B Instruct, Devstral Small 1.1+33以上 · 平均+43%高速
Unlocks 36 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 43%.
〜$499 MSRP
MacBook Pro M4 Pro 24GBAppleアップグレード
24 GB Unified (+6)273 GB/s (+123)
C
Unlocks 38 additional models that do not fit on the current setup.解放されるモデル Qwen 3.6 27B, Magistral Small 2507, Devstral Small 2 24B Instruct+35以上 · 平均+42%高速
Unlocks 38 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 42%.
〜$1,999 MSRP
Intel Arc Pro B60 24GBコスパ最良24 GB VRAM (+6)456 GB/s (+306)
A
Unlocks 72 additional models that do not fit on the current setup.解放されるモデル Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+69以上 · 平均+57%高速
Unlocks 72 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 57%.
〜$599 MSRP
AMD Instinct MI350X 288GB最大の飛躍
288 GB VRAM (+270)8000 GB/s (+7850)
B
Unlocks 117 additional models that do not fit on the current setup.解放されるモデル Qwen3-Coder 30B A3B Instruct, Qwen 3.5 397B A17B, Devstral 2 123B Instruct+114以上 · 平均+539%高速
Unlocks 117 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 539%.
〜$8,000 MSRP
Frequently Asked Questions
Can MacBook Pro M3 Pro 18GB run AI models?
Yes! MacBook Pro M3 Pro 18GB (18 GB unified memory) can run 59 models at full speed and 231 total. Top picks: Qwen 3.5 9B (score: 94/100), Qwen 3 8B (score: 92/100), Qwen 3.5 4B (score: 92/100). See the full tiered compatibility list above.
How much unified memory does MacBook Pro M3 Pro 18GB have for AI?
MacBook Pro M3 Pro 18GB has 18 GB of unified memory shared between CPU and GPU, all available for AI model inference. Unlike discrete GPUs with separate VRAM, unified memory means models can use the full 18 GB without data transfer overhead.
Is unified memory on MacBook Pro M3 Pro 18GB the same as VRAM for local AI?
Not exactly. Unified memory is excellent for making larger models fit on Apple Silicon, because the CPU and GPU share one memory pool. But it is still not identical to dedicated VRAM on a high-bandwidth discrete GPU. For local AI, unified memory often wins on flexibility and capacity, while discrete GPUs can still win on raw tokens per second once a model fits comfortably.
Is MacBook Pro M3 Pro 18GB good for running LLMs locally?
Yes, MacBook Pro M3 Pro 18GB is excellent for running LLMs locally. With 18 GB unified memory and Metal acceleration, it handles 231 models with top scores above 80/100.
Why can a smaller CUDA GPU sometimes feel faster than MacBook Pro M3 Pro 18GB for local AI?
Because fit and speed are not the same thing. MacBook Pro M3 Pro 18GB can often fit larger models thanks to unified memory, but a smaller NVIDIA GPU with fast dedicated VRAM and mature CUDA kernels can still deliver higher decode throughput once the model fits. In practice, Apple Silicon is excellent for flexible local AI on one machine, while CUDA often stays ahead for the easiest setup and highest raw inference speed.
What is the best way to run AI models on MacBook Pro M3 Pro 18GB?
We recommend using llama.cpp on MacBook Pro M3 Pro 18GB. Install it with a single command, then pull your preferred model. llama.cpp supports Metal acceleration out of the box on Apple Silicon.
What is the best coding model for MacBook Pro M3 Pro 18GB?
For coding on MacBook Pro M3 Pro 18GB, we recommend Qwen 3.5 9B. It achieves 21.4 tokens per second with 34K context window using 10.5 GB of unified memory. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Can MacBook Pro M3 Pro 18GB run Flux for image generation?
MacBook Pro M3 Pro 18GB can run Flux.1 Dev with sequential offloading or at reduced precision (FP8/NF4). The Schnell variant is faster and fits more easily in 18 GB unified memory.
What image and video AI models can I run on MacBook Pro M3 Pro 18GB?
MacBook Pro M3 Pro 18GB (18 GB unified memory) supports various AI generation tasks. For image generation, SDXL and Stable Diffusion 3.5 run well with Metal acceleration. For video, LTX Video 2.3 can generate short clips.
Is MacBook Pro M3 Pro 18GB good for AI image generation?
MacBook Pro M3 Pro 18GB is good for AI image generation. It handles SDXL and SD 3.5 well with Metal acceleration. Larger models like Flux may need offloading.
Should I upgrade from MacBook Pro M3 Pro 18GB for AI?
There are 4 upgrade path(s) from MacBook Pro M3 Pro 18GB: RTX 4060 Ti 16GB (16 GB), MacBook Pro M4 Pro 24GB (24 GB). Upgrading would unlock larger models like Qwen3-Coder 30B A3B Instruct and Qwen 3.5 397B A17B and faster inference.
Can MacBook Pro M3 Pro 18GB run Qwen 3.5?
MacBook Pro M3 Pro 18GB with 18 GB can run Qwen 3.5 4B at Q8 and Qwen 3.5 9B at Q4 (5.5 GB). The 9B variant gives strong performance for chat, coding, and multilingual tasks. Use Ollama or MLX for Metal-accelerated inference.
What are the best local LLMs for MacBook Pro M3 Pro 18GB?
The best local LLMs for MacBook Pro M3 Pro 18GB (18 GB) are: Qwen 3.5 9B (94/100, 21 tok/s), Qwen 3 8B (92/100, 24 tok/s), Qwen 3.5 4B (92/100, 48 tok/s). These models fit natively in unified memory with room for context. For coding, try the top coding pick above. For general chat, the highest-scored model gives the best Apple Silicon local AI experience.
How fast is MacBook Pro M3 Pro 18GB for local LLM performance?
MacBook Pro M3 Pro 18GB achieves 15-21 tok/s for well-fitted models with 150 GB/s memory bandwidth. Token generation speed on Apple Silicon is primarily limited by memory bandwidth and fit. Comfortable reading speed is about 6-8 tokens per second, so most natively-fitting models will feel responsive for interactive chat. MLX generally delivers 10-20% better performance than llama.cpp on newer Apple Silicon chips.
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