Mac Studio M4 vs. RTX 5090 PC: Best for Local AI?

I spent the last month living in a room that smelled like ozone and expensive solder. On one side of my desk sat the Mac Studio M4 Ultra, a silent silver cube that looks like it belongs in a museum. On the other, a custom-built PC housing the NVIDIA RTX 5090—a triple-slot monster that draws enough power to dim the lights in my home office.

The question isn’t just “which is faster?” That’s a boring question. The real question is: which one actually lets you do the work? If you’re trying to run a 405B parameter model or fine-tune a Llama 4 variant in your bedroom, you’re about to hit a wall. One of these machines lets you climb over it. The other makes you pay for the privilege in sweat and electricity bills.

The Hardware Reality Check: Specs That Matter

Forget the marketing slides. Let’s look at the guts. The RTX 5090 is built on NVIDIA’s Blackwell architecture. It’s a specialized tool designed for one thing: pushing tensors as fast as humanly possible. The Mac Studio M4 Ultra is a System-on-a-Chip (SoC). It’s a generalist that happens to have a very wide memory pipe.

The RTX 5090 comes with 32GB of GDDR7 VRAM. That sounds like a lot until you try to load a model with a 128k context window. Then it feels like a cramped apartment. The Mac Studio, however, can be configured with up to 512GB of Unified Memory. That’s the “cheat code.” In the AI world, memory capacity is often more important than compute speed. If the model doesn’t fit in the RAM, it doesn’t run. Period.

RTX 5090 PC Specs (Typical Build)

• GPU: NVIDIA RTX 5090 (32GB GDDR7)
• Memory Bandwidth: 1,792 GB/s
• AI Compute: 3,352 TOPS (FP4)
• Power Draw: 575W (GPU only) / ~900W (System)
• Interface: PCIe 5.0

Mac Studio M4 Ultra Specs

• GPU: 80-core (Integrated)
• Memory: Up to 512GB Unified Memory
• Memory Bandwidth: 800 GB/s
• AI Compute: ~38 TOPS (Neural Engine) + GPU compute
• Power Draw: ~150W (Peak system)
• Interface: Thunderbolt 5

The Memory Wall: Why VRAM is Everything

I saw this happen a dozen times: a developer buys a 5090, gets it home, and tries to run the latest Llama 3.1 405B model. They immediately hit an “Out of Memory” (OOM) error. Even with 4-bit quantization (GGUF or EXL2), that model needs over 200GB of space. A single 5090, with its 32GB, is like trying to fit a gallon of water into a shot glass.

The Mac Studio doesn’t have this problem. Because the CPU and GPU share the same pool of Unified Memory, you can allocate 75% or more of that 192GB or 512GB pool to the GPU. I ran a 70B model with a massive context window on the Mac, and it didn’t flinch. The PC would have needed four RTX 5090s linked together to match that capacity. Do you have $8,000 for GPUs and a 2000W power circuit? I didn’t think so.

Raw Speed: CUDA vs. MLX

Here’s the catch. When the model *does* fit in the 5090’s VRAM, it absolutely shreds the Mac. I tested Stable Diffusion XL and the new Flux.1 model. The 5090 generated images in under two seconds. The Mac took nearly ten. In the world of local AI, NVIDIA’s Tensor Cores are still the gold standard.

NVIDIA uses CUDA. It’s been around for over a decade. Every AI research paper, every new GitHub repo, and every Docker container is built for CUDA first. Apple has MLX. It’s a newer framework designed specifically for Apple Silicon. It’s fast, and it’s getting better, but it’s still playing catch-up. I found that many niche models require “shimming” or custom kernels to work on the Mac, whereas they just “work” on the PC.

Benchmarking Local LLMs: Tokens Per Second

I ran Llama 3.1 8B and 70B on both machines. For the 8B model, the RTX 5090 is a firehose. It spits out tokens faster than you can read them—over 150 tokens per second (t/s). The Mac Studio M4 Ultra hit about 80 t/s. Both are “fast enough,” but the 5090 feels instantaneous.

When we moved to the 70B model, things got interesting. The 5090 (using 4-bit quantization) managed about 40 t/s. The Mac Studio was slower, around 25 t/s. But here is the kicker: the Mac could handle a much larger context. If I fed the model a 50-page PDF to analyze, the 5090 would run out of VRAM halfway through the prompt. The Mac just kept chewing through it.

The Training Trap: Don’t Buy a Mac to Train

Don’t bother with a Mac if you plan on doing heavy fine-tuning. I tried running a LoRA (Low-Rank Adaptation) training session on a dataset of 10,000 documents. The PC finished in two hours. The Mac was still grinding away six hours later.

Training is about raw, brutal parallel processing. The 5090 has thousands of CUDA cores designed for this exact math. The Mac’s architecture is optimized for efficiency and inference (running the model), not the heavy lifting of backpropagation. If you’re a researcher training models from scratch, the PC isn’t just better—it’s the only real choice.

Software Ecosystem: Windows/Linux vs. macOS

If you love tinkering, the PC is your playground. You have WSL2 (Windows Subsystem for Linux), which lets you run Ubuntu inside Windows with full GPU access. Or you can just go full Linux. Most AI tools like Ollama, Text-Generation-WebUI, and ComfyUI are native to this world.

The Mac experience is different. It’s “cleaner.” You download an app like LM Studio or Faraday.dev, and it just works. But the moment you want to go off the beaten path—say, running a new multimodal model that just dropped on Hugging Face—you’ll be hunting for Metal-compatible forks. I’ve spent too many nights debugging PyTorch MPS (Metal Performance Shaders) errors. It’s getting better, but the “it just works” Apple magic doesn’t always apply to cutting-edge AI.

Power Consumption: The Silent Cost

I checked my electric meter. The RTX 5090 PC is a space heater. Under full load, my system was pulling nearly 900 watts. If you run that 24/7 for inference or training, you will see it on your monthly bill. Plus, the fans. Even with a high-end liquid cooler, the 5090 sounds like a jet taking off when it’s crunching numbers.

The Mac Studio? I forgot it was on. It pulls about 150 watts under load. It stays cool. It stays silent. If you work in a quiet office or a bedroom, this is a massive quality-of-life win. I saw my PC’s room temperature rise by five degrees after an hour of image generation. The Mac didn’t even warm up my coffee.

Build vs. Buy: The Setup Headache

Building a 5090 PC is a project. You need a massive case. You need a 1200W+ Power Supply (PSU). You need to worry about the 12VHPWR connector melting (though the 50-series supposedly fixed this). You’ll spend a weekend cable managing and installing drivers.

The Mac Studio is a box. You plug in one cable. You’re done. For some, the DIY aspect is fun. For a professional who just wants to get to work, the Mac is a relief. But remember: you can’t upgrade the Mac. If you want more RAM later, you have to buy a whole new machine. With the PC, I can swap the 5090 for a 6090 in two years. I can add more NVMe storage. I can double my system RAM for $200. Apple charges $400 just to move from 64GB to 128GB. It’s a “memory tax” that hurts.

Running the 405B Giants

Let’s talk about the “Frontier” models. Llama 3.1 405B is the current king. To run it locally on a PC, you need a multi-GPU setup. We’re talking three or four 5090s. That requires a specialized motherboard, a server-grade PSU, and probably a dedicated 20-amp circuit in your house. It’s a nightmare.

On a Mac Studio with 512GB of RAM, you just load the model. It’s slow—maybe 2 or 3 tokens per second—but it works. For a researcher who needs to verify a prompt or test a logic chain on a massive model, having that capability in a single desktop box is incredible. It’s the difference between “I can’t do this” and “I can do this slowly.”

The 2026 AI Workflow: RAG and Agents

Local AI isn’t just about chatting with a bot anymore. It’s about RAG (Retrieval-Augmented Generation). You feed your local AI 10,000 private emails or 500 code files, and it becomes an expert on *your* data. This requires a lot of system memory to hold the “embeddings” and the vector database.

The Mac Studio excels here. The massive Unified Memory pool allows you to keep the model and the entire vector database in RAM at the same time. On the PC, you’re constantly shuffling data between the system RAM and the VRAM over the PCIe bus. This creates latency. In my testing, “Time to First Token” (TTFT) was often better on the Mac for complex RAG tasks because there was no “bus tax” to pay.

Future Proofing: PCIe 5.0 vs. Thunderbolt 5

The PC has PCIe 5.0. It’s the fastest way to move data between a CPU and a GPU. If you ever decide to add an external GPU (eGPU) or a second internal card, the bandwidth is there.

The Mac Studio M4 has Thunderbolt 5. It’s a huge leap over Thunderbolt 4, doubling the bandwidth. This makes external storage for massive model libraries (which can be hundreds of gigabytes) much more viable. But let’s be real: you’re not plugging an NVIDIA GPU into a Mac. Apple’s ecosystem is a walled garden. You’re betting on Apple’s internal GPU getting better with every software update.

The Cost of Ownership

A top-tier RTX 5090 PC build will set you back about $4,000 to $5,000. A Mac Studio M4 Ultra with 192GB of RAM is roughly the same price.

The PC is “cheaper” if you already have some parts, but the 5090 itself is $2,000. The Mac is more expensive upfront but uses less power and has a higher resale value. I’ve seen three-year-old Mac Studios sell for 70% of their original price. A three-year-old PC? You’re lucky to get 40%. If you cycle your hardware every two years, the Mac might actually be the cheaper long-term play.

Use Case Scenarios

The “I want it all” Developer

Get the PC. You need Linux. You need CUDA. You need to be able to run every random script on GitHub without wondering if it supports Metal. The 32GB of VRAM is enough for 90% of development tasks, and you can always use a cloud provider for the 405B monsters.

The Privacy-Focused Researcher

Get the Mac Studio. If you’re working with sensitive data that *cannot* leave your desk, you need the ability to run the biggest, smartest models locally. The 192GB or 512GB of Unified Memory is your only way to run a 405B model without building a server farm in your closet.

The Creative Pro (Flux/Stable Diffusion)

Get the PC. Image and video generation are all about raw GPU throughput. The 5090 will save you hours of waiting every week. The Mac is fine for the occasional AI-generated headshot, but for serious production work, NVIDIA is the only game in town.

Final Verdict: Brute Force vs. Elegant Capacity

The RTX 5090 is a racecar. It’s loud, it’s thirsty, and it’s incredibly fast on a specific track. If your AI work fits within 32GB, nothing else comes close. It is the best consumer hardware ever made for machine learning.

The Mac Studio M4 Ultra is a heavy-lift cargo plane. It’s not as fast, but it carries a load that would crush the race car. It redefines what “local AI” means by bringing datacenter-scale memory to a desktop.

I’m keeping both. I use the PC to train my LoRAs and generate my images. But when I need to sit down and have a deep, 2-hour conversation with a Llama model about a complex codebase, I turn on the Mac. It’s quiet, it’s smart, and it never runs out of room to think.