The Mac vs PC for local AI question got a lot more interesting in 2026. Apple launched Mac Studios with M5 Max and M5 Ultra chips and up to 512 GB of unified memory, while PC graphics cards became painfully expensive because of the memory shortage. At the same time NVIDIA’s DGX Spark and AMD’s Ryzen AI Max machines brought unified memory to the PC side. This guide compares all four approaches for running LLMs, image models and video models at home, with real specs, prices and the trade-offs nobody mentions in the spec sheets.
- Mac wins for big LLMs per dollar: an M5 Max Mac Studio (from $2,499, up to 128 GB) or M5 Ultra (from $5,499 with 96 GB, up to 512 GB at about 1.2 TB/s) runs 70B-400B+ models that need several GPUs on a PC.
- PC wins for speed and ecosystem: NVIDIA GPUs are much faster at prompt processing, image and video generation, and everything CUDA-only (most ComfyUI nodes, training tools).
- DGX Spark: 128 GB with full CUDA, but only 273 GB/s and now $6,950.
- Ryzen AI Max: up to 128 GB (Strix Halo) or 192 GB (new Gorgon Halo) of unified memory in a mini PC; slower than Macs but runs Windows and Linux.
- My pick: chat and coding with big models on a Mac; images, video and training on an NVIDIA PC.
Unified Memory vs VRAM
A PC with a graphics card has two separate pools of memory: system RAM for the CPU and VRAM on the card. Models run fast only when they fit in VRAM, and consumer cards top out at 32 GB. A Mac (and the DGX Spark and AMD’s Ryzen AI Max chips) uses unified memory: one large pool that the CPU and GPU share. If you buy a Mac with 128 GB, the GPU can use most of it for model weights.
The trade-off is bandwidth. Token generation speed is roughly memory bandwidth divided by model size. An RTX 5090 has 1,792 GB/s; an M5 Ultra about 1.2 TB/s; an M5 Max 460-614 GB/s; a DGX Spark 273 GB/s. So unified-memory machines can hold bigger models, but GPUs run the models that fit faster. My VRAM guide shows how to work out model sizes.
The 2026 Macs for AI
Apple announced new Mac Studios and a Mac mini on 25 August 2026, with sales starting 22 September. The 512 GB M5 Ultra configuration became orderable on 28 October.
| Mac | Chip | Max unified memory | Bandwidth | Starting price |
|---|---|---|---|---|
| Mac mini | M6 | 32 GB | up to 170 GB/s | $899 (16 GB) |
| Mac mini | M5 Pro | 64 GB | 307 GB/s | $1,699 |
| Mac Studio | M5 Max | 128 GB | 460-614 GB/s | $2,499 |
| Mac Studio | M5 Ultra | 512 GB (96 GB base) | about 1.2 TB/s | $5,499 (96 GB); 512 GB from $22,299 |
Figures from PromptQuorum’s Apple Silicon guide (verified 2 November 2026). The M5 generation also added Neural Accelerators to the GPU cores, which speeds up prompt processing, historically the Mac’s weakest point. In a BGR test cited by PromptQuorum, a 256 GB M5 Ultra ran Qwen 3.8 27B at about 55 tokens/s (about 40 on M3 Ultra) and Qwen3.5-122B-A10B at about 80 tokens/s (about 60 on M3 Ultra), using 4-bit MLX models in LM Studio.
The PC Options
Desktop with NVIDIA or AMD GPUs
The classic route. Current prices (November 2026): RTX 5060 Ti 16 GB about $770, RTX 5090 32 GB about $6,900, Radeon AI PRO R9700 32 GB about $1,700-$1,850, used RTX 3090 24 GB about $1,450. For 70B models you need two or more cards. Full buying advice is in the best GPU for local LLMs.
NVIDIA DGX Spark
A small box with the GB10 chip and 128 GB of LPDDR5X unified memory at 273 GB/s, running NVIDIA’s Linux stack with full CUDA. It launched at $3,999 and now costs $6,950; a 64 GB model costs $4,999 (ServeTheHome, October 2026). LMSYS measured about 50 tokens/s decode on gpt-oss-20b, a quarter of an RTX PRO 6000. It is good for developers who need CUDA and lots of memory, not for raw speed.
AMD Ryzen AI Max (Strix Halo and Gorgon Halo)
AMD’s big APUs pair a 16-core Zen 5 CPU with a 40-CU Radeon iGPU and up to 128 GB of shared LPDDR5X (Strix Halo, Ryzen AI Max+ 395). The new Gorgon Halo refresh (Ryzen AI Max+ Pro 495 and siblings) supports up to 192 GB, with up to 160 GB usable as VRAM, according to Tom’s Hardware. These mini PCs run Windows or Linux and llama.cpp via Vulkan or ROCm. They are slower than an M5 Max but cheaper and more open.
NVIDIA RTX Spark laptops
NVIDIA’s N1X “RTX Spark” Windows-on-Arm laptops went on sale on 16 October 2026 with up to 128 GB of unified memory. They are interesting for portable local AI, but software support on Windows-on-Arm is still maturing. I cover NPUs and AI laptops in what is an NPU.
Head-to-Head Comparison
| Mac Studio M5 Max | Mac Studio M5 Ultra | PC + RTX 5090 | PC + 2x R9700 | DGX Spark | |
|---|---|---|---|---|---|
| Memory for models | up to 128 GB | 96-512 GB | 32 GB | 64 GB | 128 GB |
| Bandwidth | 460-614 GB/s | about 1.2 TB/s | 1,792 GB/s | 640 GB/s per card | 273 GB/s |
| Price (Nov 2026) | from $2,499 | from $5,499 | about $6,900 GPU + PC | about $3,500 GPUs + PC | $6,950 |
| Largest LLM at Q4 | about 120B MoE / 70B dense | 400B+ MoE | 32-49B dense | 70B dense | 120B MoE / 70B dense |
| Image/video gen | OK (slow for video) | OK | Best | Limited (no CUDA) | Good (CUDA) |
| CUDA | No | No | Yes | No | Yes |
For LLMs
For chatting and coding with large models, the Mac is the easiest win in 2026. A 128 GB M5 Max can hold gpt-oss-120b, Qwen3.5-122B-A10B or Nemotron 3.5 Super entirely in memory, and MoE models with few active parameters run quickly because each token reads only a small part of the weights. On a PC you would need four 32 GB cards or heavy RAM offload to do the same, and system RAM is now expensive too (see RAM prices in 2026).
The PC wins when the model fits in VRAM: a 27B-32B model on an RTX 5090 or R9700 is faster than on an M5 Max, and prompt processing (reading long documents or large codebases) is several times faster on NVIDIA. If you paste 50,000-token contexts into a coding agent, that difference is very noticeable.
For Image and Video Generation
This is where the PC still wins clearly. ComfyUI runs on Macs through PyTorch’s MPS backend, and many image models work, but diffusion and especially video models are compute-heavy rather than memory-heavy, and NVIDIA’s tensor cores are much faster. Many popular custom nodes, attention kernels (SageAttention, FlashAttention) and speed LoRA tricks are CUDA-only. If video generation with Wan 2.2, LTX-2.5 or MiniMax H3 is your main use, buy NVIDIA. My Wan 2.2 16 GB guide shows what a mid-range NVIDIA card can do.
Software: MLX vs CUDA
- Mac: Ollama, LM Studio and llama.cpp all support Apple Silicon. Apple’s MLX framework (and MLX models in LM Studio) is usually the fastest option and supports LoRA fine-tuning. Training anything bigger than a LoRA is limited.
- NVIDIA PC: everything works first: vLLM, SGLang, TensorRT-LLM, Unsloth, ComfyUI, kohya and every research repo.
- AMD PC / Ryzen AI Max: llama.cpp (Vulkan/ROCm), LM Studio and Ollama work well; PyTorch with ROCm works for many projects but not all.
Cost, Power and Noise
A Mac Studio draws a fraction of the power of a multi-GPU PC and is close to silent, which matters if it runs all day next to you. An RTX 5090 alone is rated at 575 W; two R9700s at 300 W each. Macs also hold resale value well. On the other hand, a PC can be upgraded one part at a time, while Mac memory is fixed at purchase, so buy more than you think you need.
Which Should You Buy?
| Your main use | Best choice |
|---|---|
| Chat and coding with 70B-120B models, quiet desk machine | Mac Studio M5 Max 128 GB |
| Running the biggest open models (200B-1T MoE) at home | Mac Studio M5 Ultra 256-512 GB |
| Image and video generation, LoRA training | NVIDIA PC (RTX 5060 Ti 16 GB to RTX 5090) |
| Cheapest way to 64 GB of fast VRAM for LLMs | PC with 2x Radeon AI PRO R9700 |
| CUDA development with lots of memory | DGX Spark |
| Windows/Linux mini PC with 128 GB | Ryzen AI Max+ 395 mini PC |
| Occasional big jobs | Rent: cheapest cloud GPUs |
My own setup is a PC with two RTX 5080s and 128 GB of RAM, which is great for ComfyUI and fine for MoE models with offload, but a 128 GB Mac would run the big LLMs more simply. Many people end up with both: a Mac for language models and a PC for everything visual.
Getting Started on Each Platform
| Step | Mac (Apple Silicon) | Windows/Linux PC (NVIDIA) |
|---|---|---|
| Chat with a local model | Install Ollama from ollama.com or LM Studio; in LM Studio pick MLX versions of models | Install Ollama or LM Studio; CUDA is used automatically |
| First command | ollama run gemma4:12b |
ollama run gemma4:12b |
| Image and video | ComfyUI Desktop for macOS (uses Metal/MPS) | ComfyUI Portable (Windows) or manual install |
| Check memory use | Activity Monitor, Memory tab (watch memory pressure) | nvidia-smi or Task Manager, GPU tab |
| Fine-tuning | pip install mlx-lm for LoRA |
Unsloth or kohya |
On a Mac you can let the GPU use more of the unified memory with sudo sysctl iogpu.wired_limit_mb= followed by a value in MB (for example 110000 on a 128 GB machine). Leave enough for macOS, and note the setting resets on reboot.
Common Problems on Mac and PC
| Problem | Mac fix | PC fix |
|---|---|---|
| Model will not load | Model bigger than the GPU memory limit; pick a smaller quant or raise the wired limit | Model bigger than VRAM; use a smaller quant or allow partial CPU offload |
| Very slow replies | Memory pressure is red and the Mac is swapping; close apps or use a smaller model | Model spilled into system RAM; check GPU memory in nvidia-smi |
| ComfyUI node errors | Some custom nodes need CUDA; look for MPS-compatible versions | Update ComfyUI and install missing nodes with ComfyUI Manager |
| Long prompts are slow to start | Prompt processing is the Mac’s weak spot; shorten context or use prompt caching in LM Studio | Usually fast; check Flash Attention is on |
Frequently Asked Questions
Is a Mac good for running local AI?
Yes, especially for large language models. Unified memory lets an M5 Max or M5 Ultra Mac Studio run models that need several GPUs on a PC. It is slower for image and video generation and lacks CUDA.
Is the M5 Ultra faster than an RTX 5090?
Not for models that fit in 32 GB: the RTX 5090 has more bandwidth (1,792 GB/s vs about 1.2 TB/s) and much faster compute. The M5 Ultra wins when the model is bigger than 32 GB, because it can hold it entirely in memory.
How much unified memory do I need on a Mac for LLMs?
64 GB for 27B-32B models with long context, 128 GB for 70B dense or 120B MoE models, and 256-512 GB for the largest open models.
Is the DGX Spark better than a Mac Studio?
The DGX Spark has full CUDA support, which matters for developers and image/video tools. The Mac Studio M5 Max has about twice the bandwidth for less money, so it generates text faster.
Can I run ComfyUI on a Mac?
Yes, using the MPS backend, and many image workflows work. Video models are slow, and CUDA-only custom nodes will not run.
Can I use an external GPU with a Mac for AI?
No. Apple Silicon Macs do not support external GPUs, so the unified memory you buy is the memory you have for AI.
Final Thoughts
In 2026 the answer depends on what you generate. For words, the Mac’s unified memory is the best value at the high end, especially with GPU prices where they are. For pixels, NVIDIA is still the standard. Whichever you choose, start with Ollama on Windows, Mac or Linux and compare runtimes in Ollama vs LM Studio vs llama.cpp.
Sources
- PromptQuorum: Local LLMs on Apple Silicon 2026 (verified 2 Nov 2026)
- ServeTheHome: DGX Spark 64 GB and price increase
- Spheron: DGX Spark vs RTX PRO 6000 (LMSYS figures)
- Tom’s Hardware: AMD Ryzen AI Max 400 Gorgon Halo
- Shattered: RTX Spark vs Snapdragon X2 vs Panther Lake vs Ryzen AI 400 (3 Nov 2026)
- Tech Insider: GPU prices (Nov 2026)
