397BTier 100Needs ~254.9 GB
Apple
Operating mode
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.
Apple Silicon local AI performance. Excellent for local AI. Your MacBook Pro M2 Max 96GB with 96 GB unified memory can run 134 models natively, 195 more with limits. The best match is Qwen 3.6 35B A3B at 32 tok/s for interactive local LLM use.
134
Run great
329
Total compatible
111B
Max parameters
32
Best tok/sEST.
Comparison guide
Top models ranked for coding, chat, and writing with FAQ and buyer guidance — the comparison-intent companion to this spec sheet.
Cost vs cloud API
Assumes 4 hours/day of active inference at 32 tok/s, MacBook Pro M2 Max 96GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
14.0M
Tokens/month at this pace
$106
Monthly local cost
$140
Same tokens on cloud API
$7.60
Local $/1M tokens
Break-even: amortizes in 27.3 months vs cloud API. Price reference: $3.8k (used).
Quick picks
Top recommendations for common local AI workloads on your MacBook Pro M2 Max 96GB
MacBook Pro M2 Max 96GB with 96 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
Every model ranked by how well it runs on your MacBook Pro M2 Max 96GB, grouped by fit quality
These models fit comfortably and run at full speed on your Mac.
These models run but may need quantization or have reduced context windows.
These models are too large for your Mac's unified memory.
Beyond LLMs
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) | Runs natively | Qwen 3 30B Q4 |
| LLM Large (70B) | Runs natively | Llama 3.1 70B Q4 |
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 |
| Video Short (25f) | Runs natively | LTX Video 2B |
| Video Long (100f) | Runs with sequential offload | Wan Video 14B |
Same chip, more memory
Compare M2 configurations to see which models become available
16 GB unified memory
59
Run great
212
Total fit
Architecture
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.
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
The best local LLM for each task on your MacBook Pro M2 Max 96GB
Chat
SThis 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.
Coding
SThis model is a direct match for coding. 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.
Agentic Coding
SThis 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 should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.
Reasoning
SThis 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.
RAG
SThis model is a direct match for rag. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.
Just out of reach
High-quality models that need a bit more memory
Image & Video Generation
51 of 52 models can generate images or video on your MacBook Pro M2 Max 96GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~5.4s | S |
| Stable Diffusion 1.5Image | 512×768 | ~10.7s | S |
| Realistic Vision v5.1Image | 512×768 | ~10.7s | S |
| DreamShaper 8Image | 512×768 | ~10.7s | S |
| LCM DreamShaper v7Image | 512×768 | ~3.2s | S |
| PixArt-SigmaImage | 1024×1024 | ~43s | S |
| FramePack I2VVideo | 1280×720 | ~1m 19s/frame | S |
| SDXL TurboImage | 512×512 | ~5.4s | S |
| SDXL LightningImage | 1024×1024 | ~16.1s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~43s | S |
| Playground v2.5Image | 1024×1024 | ~1m 5s | S |
| RealVisXL v5.0Image | 1024×1024 | ~48.4s | S |
| DreamShaper XLImage | 1024×1024 | ~48.4s | S |
| Juggernaut XL v9Image | 1024×1024 | ~48.4s | S |
| Animagine XL 3.1Image | 1024×1024 | ~48.4s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~48.4s | S |
| Animagine XL 4.0Image | 1024×1024 | ~48.4s | S |
| Illustrious XLImage | 1024×1024 | ~48.4s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~31.4s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~1m 15s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~12.9s | S |
| LTX Video 2BVideo | 1280×720 | ~37.3s/frame | S |
| KolorsImage | 1024×1024 | ~1m 26s | S |
| Stable CascadeImage | 1024×1024 | ~1m 47s | S |
| AuraFlow v0.3Image | 1536×1536 | ~3m 13s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~3m 56s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~43s | S |
| CogVideoX 2BVideo | 720×480 | ~37.3s/frame | S |
| HunyuanVideoVideo | 720×1280 | ~1m 19s/frame | S |
| ChromaImage | 1024×1024 | ~43s | S |
| Z-Image TurboImage | 1536×1536 | ~44.3s | S |
| Flux.1 DevImage | 1024×1024 | ~3m 13s | S |
| Flux.1 SchnellImage | 1024×1024 | ~37.6s | S |
| LTX Video 13BVideo | 1280×720 | ~1m 19s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~3m 35s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~19.6s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~1m 2s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~53.8s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~53.8s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | ~21.5s | S |
| Flux.1 Fill DevImage | 1024×1024 | ~3m 3s | S |
| Mochi 1 PreviewVideo | 848×480 | ~1m 11s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~1m 6s/frame | S |
| Helios 14BVideo | 1280×720 | ~1m 21s/frame | S |
| SkyReels V2 14BVideo | 1280×720 | ~1m 21s/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | ~1m 21s/frame | A |
| Wan Video 2.2 14BVideo | 720×1280 | ~1m 21s/frame | A |
| Qwen ImageImage | 1024×1024 | ~1m 12s | A |
| Qwen Image EditImage | 1024×1024 | ~1m 12s | A |
| Flux.2 DevImage | 256×256 | ~54m 1s | B |
| MAGI-1Video | 848×480 | ~1m 41s/frame | B |
| HunyuanImage 3.0Image | 256×256 | ~2m 7s | 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
Everything you need to start running models locally with Metal acceleration and Apple Silicon unified memory
Ollama runs natively on macOS with Metal GPU acceleration. One command to install.
curl -fsSL https://ollama.com/install.sh | shQwen 3.6 35B A3B is the best match for your MacBook Pro M2 Max 96GB. Pull and run it:
ollama run qwen:3.6:35b:a3bUpgrade paths
See what you unlock with more unified memory
Upgrade options
NVIDIA A100 80GBNext step upUnlocks 6 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 181%.
~$15,000 MSRP
Unlocks 7 additional models that do not fit on the current setup.
~$2,499 MSRP
Unlocks 7 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 48%.
~$4,999 MSRP
Unlocks 20 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 349%.
~$8,000 MSRP
Yes! MacBook Pro M2 Max 96GB (96 GB unified memory) can run 134 models at full speed and 329 total. Top picks: Qwen 3.6 35B A3B (score: 92/100), Qwen3-Coder 30B A3B Instruct (score: 92/100), Qwen 3.5 35B A3B (score: 91/100). See the full tiered compatibility list above.
MacBook Pro M2 Max 96GB has 96 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 96 GB without data transfer overhead.
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.
Yes, MacBook Pro M2 Max 96GB is excellent for running LLMs locally. With 96 GB unified memory and Metal acceleration, it handles 329 models with top scores above 80/100.
Because fit and speed are not the same thing. MacBook Pro M2 Max 96GB 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.
We recommend using llama.cpp on MacBook Pro M2 Max 96GB. Install it with a single command, then pull your preferred model. llama.cpp supports Metal acceleration out of the box on Apple Silicon.
For coding on MacBook Pro M2 Max 96GB, we recommend Qwen3-Coder-Next. It achieves 17.2 tokens per second with 99K context window using 61.5 GB of unified memory. This model is a direct match for coding. 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.
Yes, MacBook Pro M2 Max 96GB with 96 GB unified memory can run Flux.1 Dev at FP16. Use ComfyUI or Draw Things for the best experience on macOS.
MacBook Pro M2 Max 96GB (96 GB unified memory) supports various AI generation tasks. For image generation, SDXL and Stable Diffusion 3.5 run well with Metal acceleration. Flux.1 Dev also runs natively. For video, LTX Video 2.3 can generate short clips.
MacBook Pro M2 Max 96GB is excellent for AI image generation. With 96 GB unified memory and Metal GPU acceleration, it runs all major diffusion models including Flux.1, SDXL, and SD 3.5.
There are 4 upgrade path(s) from MacBook Pro M2 Max 96GB: NVIDIA A100 80GB (80 GB), MacBook Pro M3 Max 128GB (128 GB). Upgrading would unlock larger models like Qwen 3.5 397B A17B and Devstral 2 123B Instruct and faster inference.
Yes, MacBook Pro M2 Max 96GB with 96 GB can run Qwen 3.5 27B at near-lossless Q8 quantization and the 35B-A3B MoE variant comfortably at Q4. For maximum quality, the 27B dense model at Q8 is the best choice. Both MLX and Ollama (llama.cpp) support Qwen 3.5 on Mac.
The best local LLMs for MacBook Pro M2 Max 96GB (96 GB) are: Qwen 3.6 35B A3B (92/100, 32 tok/s), Qwen3-Coder 30B A3B Instruct (92/100, 35 tok/s), Qwen 3.5 35B A3B (91/100, 35 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.
MacBook Pro M2 Max 96GB achieves 23-32 tok/s for well-fitted models with 400 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.
Compare with similar
Related guides