397BTier 100Needs ~272.2 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 Mac Studio M3 Ultra 256GB with 256 GB unified memory can run 147 models natively, 198 more with limits. The best match is Qwen 3.5 122B A10B at 35 tok/s for interactive local LLM use.
147
Run great
345
Total compatible
284B
Max parameters
35
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 35 tok/s, Mac Studio M3 Ultra 256GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).
15.0M
Tokens/month at this pace
$224
Monthly local cost
$150
Same tokens on cloud API
$14.9
Local $/1M tokens
Break-even: long amortization at this workload — local is still the privacy/latency play. Price reference: $8.0k (Mac Studio M3 Ultra 256GB).
Quick picks
Top recommendations for common local AI workloads on your Mac Studio M3 Ultra 256GB
Mac Studio M3 Ultra 256GB with 256 GB unified memory. Third-generation Apple Silicon built on 3nm process with dynamic caching GPU architecture, significantly improving AI inference efficiency.
All 380 models tested
Every model ranked by how well it runs on your Mac Studio M3 Ultra 256GB, 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 natively | Wan Video 14B |
Same chip, more memory
Compare M3 configurations to see which models become available
18 GB unified memory
60
Run great
233
Total fit
Architecture
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.
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
The best local LLM for each task on your Mac Studio M3 Ultra 256GB
Chat
SQwen 3.5 122B A10B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Coding
SDevstral 2 123B Instruct is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Agentic Coding
SDevstral 2 123B Instruct is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Reasoning
SDevstral 2 123B Instruct matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
RAG
SQwen 3.5 122B A10B matches RAG and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Just out of reach
High-quality models that need a bit more memory
Image & Video Generation
56 of 56 models can generate images or video on your Mac Studio M3 Ultra 256GB
| Model | Max Resolution | Gen Time | Grade |
|---|---|---|---|
| SD TurboImage | 512×512 | ~4.6s | S |
| Stable Diffusion 1.5Image | 512×768 | ~9.2s | S |
| Realistic Vision v5.1Image | 512×768 | ~9.2s | S |
| DreamShaper 8Image | 512×768 | ~9.2s | S |
| LCM DreamShaper v7Image | 512×768 | ~2.7s | S |
| PixArt-SigmaImage | 1024×1024 | ~36.7s | S |
| FramePack I2VVideo | 1280×720 | ~1m 7s/frame | S |
| SDXL TurboImage | 512×512 | ~4.6s | S |
| SDXL LightningImage | 1024×1024 | ~13.7s | S |
| Stable Diffusion XL 1.0Image | 1024×1024 | ~36.7s | S |
| Playground v2.5Image | 1024×1024 | ~55s | S |
| RealVisXL v5.0Image | 1024×1024 | ~41.2s | S |
| DreamShaper XLImage | 1024×1024 | ~41.2s | S |
| Juggernaut XL v9Image | 1024×1024 | ~41.2s | S |
| Animagine XL 3.1Image | 1024×1024 | ~41.2s | S |
| Pony Diffusion V6 XLImage | 1024×1024 | ~41.2s | S |
| Animagine XL 4.0Image | 1024×1024 | ~41.2s | S |
| Illustrious XLImage | 1024×1024 | ~41.2s | S |
| Wan Video 2.1 1.3BVideo | 480×832 | ~26.8s/frame | S |
| Stable Diffusion 3.5 MediumImage | 1024×1024 | ~1m 4s | S |
| Flux.2 Klein 4BImage | 1024×1024 | ~11s | S |
| LTX Video 2BVideo | 1280×720 | ~31.8s/frame | S |
| KolorsImage | 1024×1024 | ~1m 13s | S |
| Stable CascadeImage | 1024×1024 | ~1m 32s | S |
| AuraFlow v0.3Image | 1536×1536 | ~2m 45s | S |
| Stable Diffusion 3.5 LargeImage | 1024×1024 | ~3m 22s | S |
| Stable Diffusion 3.5 Large TurboImage | 1024×1024 | ~36.7s | S |
| CogVideoX 2BVideo | 720×480 | ~31.8s/frame | S |
| HunyuanVideoVideo | 720×1280 | ~1m 7s/frame | S |
| ChromaImage | 1024×1024 | ~36.7s | S |
| Z-Image TurboImage | 1536×1536 | ~37.8s | S |
| Flux.1 DevImage | 1024×1024 | ~2m 45s | S |
| Flux.1 SchnellImage | 1024×1024 | ~32.1s | S |
| LTX Video 13BVideo | 1280×720 | ~1m 7s/frame | S |
| Flux.1 Kontext DevImage | 1024×1024 | ~3m 3s | S |
| AnimateDiff v1.5.3Video | 512×768 | ~16.7s/frame | S |
| Cosmos Diffusion 7BVideo | 1024×576 | ~52.5s/frame | S |
| CogVideoX 5BVideo | 720×480 | ~45.9s/frame | S |
| Wan2.2 TI2V 5BVideo | 832×480 | ~45.9s/frame | S |
| Flux.2 Klein 9BImage | 1024×1024 | ~18.3s | S |
| Flux.1 Fill DevImage | 1024×1024 | ~2m 36s | S |
| Krea 2Image | 1024×1024 | ~49.9s | S |
| Sulphur 2Video | 1280×720 | ~58.1s/frame | S |
| Ideogram 4Image | 2048×2048 | ~45.1s | S |
| Mochi 1 PreviewVideo | 848×480 | ~1m 1s/frame | S |
| HunyuanVideo 1.5Video | 720×1280 | ~56.2s/frame | S |
| Helios 14BVideo | 1280×720 | ~1m 9s/frame | S |
| SkyReels V2 14BVideo | 1280×720 | ~1m 9s/frame | S |
| Wan Video 2.1 14BVideo | 720×1280 | ~1m 9s/frame | S |
| Wan Video 2.2 14BVideo | 720×1280 | ~1m 9s/frame | S |
| Qwen ImageImage | 1024×1024 | ~1m 2s | S |
| Qwen Image EditImage | 1024×1024 | ~1m 2s | S |
| LTX-2 22BVideo | 1920×1088 | ~1m 23s/frame | S |
| Flux.2 DevImage | 1024×1024 | ~28m 54s | S |
| MAGI-1Video | 1280×720 | ~1m 26s/frame | S |
| HunyuanImage 3.0Image | 1024×1024 | ~1m 49s | B |
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 3.5 122B A10B is the best match for your Mac Studio M3 Ultra 256GB. Pull and run it:
ollama run qwen3.5:122b-a10bUpgrade paths
See what you unlock with more unified memory
Upgrade options
Unlocks 4 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 133%.
~$20,000 MSRP
Unlocks 5 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 165%.
~$8,000 MSRP
Yes! Mac Studio M3 Ultra 256GB (256 GB unified memory) can run 147 models at full speed and 345 total. Top picks: Qwen 3.5 122B A10B (score: 93/100), Mistral Small 4 119B (score: 92/100), DeepSeek V4 Flash (score: 91/100). See the full tiered compatibility list above.
Mac Studio M3 Ultra 256GB has 256 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 256 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, Mac Studio M3 Ultra 256GB is excellent for running LLMs locally. With 256 GB unified memory and Metal acceleration, it handles 345 models with top scores above 80/100.
Because fit and speed are not the same thing. Mac Studio M3 Ultra 256GB 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 Mac Studio M3 Ultra 256GB. 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 Mac Studio M3 Ultra 256GB, we recommend Devstral 2 123B Instruct. It achieves 6.3 tokens per second with 164K context window using 134.8 GB of unified memory. Devstral 2 123B Instruct is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Yes, Mac Studio M3 Ultra 256GB with 256 GB unified memory can run Flux.1 Dev at FP16. Use ComfyUI or Draw Things for the best experience on macOS.
Mac Studio M3 Ultra 256GB (256 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.
Mac Studio M3 Ultra 256GB is excellent for AI image generation. With 256 GB unified memory and Metal GPU acceleration, it runs all major diffusion models including Flux.1, SDXL, and SD 3.5.
There are 2 upgrade path(s) from Mac Studio M3 Ultra 256GB: AMD Instinct MI325X 256GB (256 GB), AMD Instinct MI350X 288GB (288 GB). Upgrading would unlock larger models like Qwen 3.5 397B A17B and Kimi K2.5 and faster inference.
Yes, Mac Studio M3 Ultra 256GB with 256 GB unified memory can run Qwen 3.5 122B-A10B (MoE) at Q4 and Qwen 3.5 27B at full FP16 precision. The MoE architecture activates only 10B parameters per token, giving excellent inference speed despite the large model size. Use MLX or Ollama for best results.
The best local LLMs for Mac Studio M3 Ultra 256GB (256 GB) are: Qwen 3.5 122B A10B (93/100, 35 tok/s), Mistral Small 4 119B (92/100, 38 tok/s), Qwen3-Coder 30B A3B Instruct (90/100, 84 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.
Mac Studio M3 Ultra 256GB achieves 24-35 tok/s for well-fitted models with 819 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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