30.5BNivel 100Necesita ~22.8 GB
Apple
Mac mini M2 24GB
M2DesktopM2UNIFIEDMetal
24GB
Unified Memory
100GB/s
Bandwidth
$1,199 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 Mac mini M2 24GB
Apple Silicon local AI performance. Excellent for local AI. Your Mac mini M2 24GB with 24 GB unified memory can run 76 models natively, 181 more with limits. The best match is Qwen 3.5 9B at 13 tok/s for interactive local LLM use.
76
Run great
257
Total compatible
24B
Max parameters
13
Best tok/sEST.
Comparison guide
Best Local LLMs for Mac mini M2 24GB — 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 Mac mini M2 24GB
About Mac mini M2 24GB for AI
Mac mini M2 24GB with 24 GB unified memory. Second-generation Apple Silicon with improved GPU performance and memory bandwidth, offering a strong balance of efficiency and AI capability.
All 374 models tested
Model Compatibility Tiers
Every model ranked by how well it runs on your Mac mini M2 24GB, grouped by fit quality
Runs Great (76 models)
These models fit comfortably and run at full speed on your Mac.
Runs with Limits (191 models)
These models run but may need quantization or have reduced context windows.
Won't Fit (107 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) | Needs offload | 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) | Runs with sequential offload | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | 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 M2 configurations to see which models become available
MacBook Pro M2 Pro 16GB
16 GB unified memory
59
Run great
212
Total fit
MacBook Pro M2 Pro 32GB
32 GB unified memory
89
Run great
292
Total fit
Unlocks: Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B
good-unified-memorymlx-optimized
Especificaciones
Cómputo
ArquitecturaM2
Memoria
Memoria unificada24 GB
Ancho de banda100 GB/s
General
FamiliaM2
SegmentoDesktop
InterconexiónUNIFIED
Plataforma de cómputoMETAL
MSRP$1,199
Características clave
M2 chip (2nd-gen 5nm TSMC)24 GB unified memory (shared CPU/GPU/Neural Engine)100 GB/s memory bandwidth16-core Neural EngineMetal 3 GPU compute (MLX framework)
Para cargas de trabajo de IA
Fortalezas
- Improved memory bandwidth over M1 (~50% increase)
- Unified memory architecture ideal for LLM inference
- Strong MLX ecosystem support
- Excellent performance per watt
Consideraciones
- Still limited by memory capacity in base configurations
- Lower bandwidth than discrete datacenter GPUs
Architecture
M2
Apple M2 is the second generation of Apple Silicon, with improved GPU cores and higher memory bandwidth. The M2 Ultra scales to 192 GB unified memory via UltraFusion die-to-die interconnect.
AI Relevance
Higher memory bandwidth (~50% more than M1 in Ultra config) directly improves token generation speed for LLMs. The M2 Ultra with 192 GB unified memory can run 70B models at full Q4 quantization with good performance.
Process: TSMC 5nm (2nd gen)Platform: METALPrecisions: FP32, FP16
M2 brings a 10-core GPU with improved memory bandwidth. The 100 GB/s bandwidth in base models and up to 200 GB/s in Pro/Max variants provides solid decode throughput for local LLMs.
All workloads
Recommendations by Workload
The best local LLM for each task on your Mac mini M2 24GB
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.
12.7 tok/s · 60K ctx · llama.cpp
10.1 GB / 24.0 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.
12.7 tok/s · 60K ctx · llama.cpp
11.2 GB / 24.0 GB Unified Memory
Agentic Coding
SQwen 3.5 9B
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.
12.7 tok/s · 60K ctx · llama.cpp
13.4 GB / 24.0 GB Unified Memory
Reasoning
SQwen 3 14B
This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
8.2 tok/s · 34K ctx · llama.cpp
14.5 GB / 24.0 GB Unified Memory
RAG
AGranite 4.1 8B
This model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.
14.3 tok/s · 58K ctx · llama.cpp
13.3 GB / 24.0 GB Unified Memory
Casi al alcance
Modelos que podrías ejecutar con una mejora
Modelos de alta calidad que necesitan un poco más de memoria
397BNivel 100Necesita ~247.1 GB
123BNivel 100Necesita ~81.2 GB
Funciona en NVIDIA DGX Spark 128GB
1000BNivel 100Necesita ~617.2 GB
1000BNivel 100Necesita ~617.2 GB
Image & Video Generation
Diffusion Model Compatibility
28 of 52 models can generate images or video on your Mac mini M2 24GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~4.8s | S |
| Stable Diffusion 1.5Image | 512×768 | ~9.6s | S |
| Realistic Vision v5.1Image | 512×768 | ~9.6s | S |
| DreamShaper 8Image | 512×768 | ~9.6s | S |
| LCM DreamShaper v7Image | 512×768 | ~2.9s | S |
| PixArt-SigmaImage | 1024×1024 | ~38.4s | S |
| SDXL TurboImage | 512×512 | ~4.8s | S |
| SDXL LightningImage | 1024×1024 | ~14.4s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~38.4s | S |
| Playground v2.5Image | 1024×1024 | ~57.5s | S |
| RealVisXL v5.0Image | 1024×1024 | ~43.1s | S |
| DreamShaper XLImage | 1024×1024 | ~43.1s | S |
| Juggernaut XL v9Image | 1024×1024 | ~43.1s | S |
| Animagine XL 3.1Image | 1024×1024 | ~43.1s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~43.1s | S |
| Animagine XL 4.0Image | 1024×1024 | ~43.1s | S |
| Illustrious XLImage | 1024×1024 | ~43.1s | S |
| Wan Video 2.1 1.3BVideo | 256×256 | ~28s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~1m 7s | S |
| Flux.2 Klein 4BImage | 256×256 | ~25.9s | S |
| LTX Video 2BVideo | 256×256 | ~1m 40s/frame | S |
| KolorsImage | 256×256 | ~3m 24s | S |
| Stable CascadeImage | 1024×1024 | ~1m 36s | B |
| AuraFlow v0.3Image | 1024×1024 | ~2m 53s | B |
| Stable Diffusion 3.5 LargeImage | 256×256 | ~9m 30s | B |
| Stable Diffusion 3.5 Large TurboImage | 256×256 | ~1m 44s | B |
| CogVideoX 2BVideo | 256×256 | ~1m 40s/frame | D |
| Z-Image TurboImage | 256×256 | ~1m 19s | D |
| FramePack I2VVideo | 256×256 | ~1m 10s/frame | F |
| HunyuanVideoVideo | 256×256 | ~1m 10s/frame | F |
| ChromaImage | 256×256 | ~38.4s | F |
| Flux.1 DevImage | 256×256 | ~2m 53s | F |
| Flux.1 SchnellImage | 256×256 | ~33.6s | F |
| LTX Video 13BVideo | 256×256 | ~1m 10s/frame | F |
| Flux.1 Kontext DevImage | 256×256 | ~3m 12s | F |
| AnimateDiff v1.5.3Video | 512×768 | ~17.5s/frame | F |
| Cosmos Diffusion 7BVideo | 256×256 | ~1m 46s/frame | F |
| CogVideoX 5BVideo | 256×256 | ~48s/frame | F |
| Wan2.2 TI2V 5BVideo | 256×256 | ~48s/frame | F |
| Flux.2 Klein 9BImage | 256×256 | ~19.2s | F |
| Flux.1 Fill DevImage | 256×256 | ~2m 43s | F |
| Mochi 1 PreviewVideo | 256×256 | ~1m 3s/frame | F |
| HunyuanVideo 1.5Video | 256×256 | ~58.8s/frame | F |
| Helios 14BVideo | 256×256 | ~1m 13s/frame | F |
| SkyReels V2 14BVideo | 256×256 | ~1m 13s/frame | F |
| Wan Video 2.1 14BVideo | 256×256 | ~1m 13s/frame | F |
| Wan Video 2.2 14BVideo | 256×256 | ~1m 13s/frame | F |
| Qwen ImageImage | 256×256 | ~1m 5s | F |
| Qwen Image EditImage | 256×256 | ~1m 5s | F |
| Flux.2 DevImage | 256×256 | ~30m 14s | F |
| MAGI-1Video | 256×256 | ~1m 30s/frame | F |
| HunyuanImage 3.0Image | 256×256 | ~1m 54s | 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 Mac mini M2 24GB
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 Mac mini M2 24GB. Pull and run it:
ollama run qwen3.5:9bWhat to expect: With 24 GB unified memory, your top models will run at 13-14-29 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 Mac mini M2 24GB
See what you unlock with more unified memory
Opciones de mejora
Opciones de mejora
RTX 4000 Ada 20GBSiguiente nivel360 GB/s (+260)
B
Desbloquea 12 modelos adicionales que hoy no caben en tu setup.Desbloquea Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen3-VL 30B A3B Instruct+9 más · +193% más rápido promedio
Desbloquea 12 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 193% en los modelos que sí caben.
~$1,250 MSRP
MacBook Pro M1 Pro 32GBMejora Apple
32 GB Unified (+8)200 GB/s (+100)
B
Desbloquea 29 modelos adicionales que hoy no caben en tu setup.Desbloquea Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen3-VL 30B A3B Instruct+26 más · +57% más rápido promedio
Desbloquea 29 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 57% en los modelos que sí caben.
~$1,999 MSRP
Intel Arc Pro B60 24GBMejor relación calidad-precio456 GB/s (+356)
A
Desbloquea 34 modelos adicionales que hoy no caben en tu setup.Desbloquea Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen3-VL 30B A3B Instruct+31 más · +152% más rápido promedio
Desbloquea 34 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 152% en los modelos que sí caben.
~$599 MSRP
AMD Instinct MI350X 288GBMayor salto
288 GB VRAM (+264)8000 GB/s (+7900)
B
Desbloquea 79 modelos adicionales que hoy no caben en tu setup.Desbloquea Qwen3-Coder 30B A3B Instruct, Qwen 3.5 397B A17B, Devstral 2 123B Instruct+76 más · +925% más rápido promedio
Desbloquea 79 modelos adicionales que hoy no caben en tu setup.
Eleva la velocidad media de decodificación en torno a un 925% en los modelos que sí caben.
~$8,000 MSRP
Frequently Asked Questions
Can Mac mini M2 24GB run AI models?
Yes! Mac mini M2 24GB (24 GB unified memory) can run 76 models at full speed and 257 total. Top picks: Qwen 3.5 9B (score: 91/100), Qwen 3 8B (score: 89/100), Qwen 3.5 4B (score: 88/100). See the full tiered compatibility list above.
How much unified memory does Mac mini M2 24GB have for AI?
Mac mini M2 24GB has 24 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 24 GB without data transfer overhead.
Is unified memory on Mac mini M2 24GB 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 Mac mini M2 24GB good for running LLMs locally?
Yes, Mac mini M2 24GB is excellent for running LLMs locally. With 24 GB unified memory and Metal acceleration, it handles 257 models with top scores above 80/100.
Why can a smaller CUDA GPU sometimes feel faster than Mac mini M2 24GB for local AI?
Because fit and speed are not the same thing. Mac mini M2 24GB 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 Mac mini M2 24GB?
We recommend using llama.cpp on Mac mini M2 24GB. 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 Mac mini M2 24GB?
For coding on Mac mini M2 24GB, we recommend Qwen 3.5 9B. It achieves 12.7 tokens per second with 60K context window using 11.2 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 Mac mini M2 24GB run Flux for image generation?
Mac mini M2 24GB can run Flux.1 Dev with sequential offloading or at reduced precision (FP8/NF4). The Schnell variant is faster and fits more easily in 24 GB unified memory.
What image and video AI models can I run on Mac mini M2 24GB?
Mac mini M2 24GB (24 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 Mac mini M2 24GB good for AI image generation?
Mac mini M2 24GB 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 Mac mini M2 24GB for AI?
There are 4 upgrade path(s) from Mac mini M2 24GB: RTX 4000 Ada 20GB (20 GB), MacBook Pro M1 Pro 32GB (32 GB). Upgrading would unlock larger models like Qwen3-Coder 30B A3B Instruct and Qwen 3.5 397B A17B and faster inference.
Can Mac mini M2 24GB run Qwen 3.5?
Yes, Mac mini M2 24GB with 24 GB can run Qwen 3.5 9B at Q8 (near-lossless, ~9.6 GB) and Qwen 3.5 27B at Q4 with limited context. For best results on Mac, use MLX or Ollama.
What are the best local LLMs for Mac mini M2 24GB?
The best local LLMs for Mac mini M2 24GB (24 GB) are: Qwen 3.5 9B (91/100, 13 tok/s), Qwen 3 8B (89/100, 14 tok/s), Qwen 3.5 4B (88/100, 29 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 Mac mini M2 24GB for local LLM performance?
Mac mini M2 24GB achieves 9-13 tok/s for well-fitted models with 100 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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