TL;DR: The Mac Studio M4 Max is quieter, simpler, and fits 70B models better. The RTX 4090 PC is faster on models that fit in 24GB and costs less. The right answer depends entirely on which models you're running and how much you value simplicity.
This comparison comes up constantly: should I build a custom PC with an RTX 4090, or just get a Mac Studio? Both are legitimate setups. Both have real tradeoffs. Here's how they actually stack up for local LLM inference.
The Configs Being Compared
Mac Studio M4 Max — 64GB unified memory:
- ~$1,999 (base M4 Max) or ~$2,999 (64GB configuration)
- 40-core GPU, 410 GB/s memory bandwidth
- 64GB unified memory usable for LLM inference
- No display included
Custom PC — RTX 4090 24GB:
- GPU: RTX 4090 used ~$1,400–1,700
- CPU: Ryzen 9 7900X ~$300
- Motherboard: ~$250
- RAM: 32GB DDR5 ~$120
- NVMe 2TB ~$120
- PSU 1000W ~$160
- Case + cooler ~$150
- Total: ~$2,500–2,700
So the fully-loaded Mac Studio 64GB at ~$2,999 is roughly comparable in total cost to a well-spec'd RTX 4090 custom PC at ~$2,500–2,700. They're in the same price range. The question is what you get for that money.
VRAM and Model Access: Mac Wins Here
This is the clearest difference between the two setups.
Mac Studio M4 Max 64GB:
- 64GB unified memory available for model inference
- Llama 3.1 70B at Q4_K_M: fits comfortably (~40GB)
- Llama 3.1 70B at Q6: fits (~55GB)
- Can run multiple models simultaneously (a 13B and a 7B at the same time)
- 72B Qwen models at Q4: yes
RTX 4090 PC:
- 24GB GDDR6X for model inference
- Llama 3.1 70B at Q4_K_M: needs heavy quantization (Q2/Q3) to fit in 24GB
- Quality of 70B inference is noticeably lower due to over-quantization
- Can run 34B at Q4 comfortably — that's the practical ceiling for quality inference
If running 70B models at real quality is your goal, the Mac Studio wins the comparison outright. It's not close — 64GB vs 24GB is a massive difference for large model inference.
Raw Inference Speed: PC Wins Here
For models that fit within 24GB — 7B through 34B — the RTX 4090 is faster.
Approximate tokens/second comparison, Llama 3.1 8B Q4_K_M:
- Mac Studio M4 Max 64GB: ~130–160 T/s
- RTX 4090: ~120–150 T/s (similar)
Approximate tokens/second, Qwen 2.5 32B Q4_K_M:
- Mac Studio M4 Max 64GB: ~45–60 T/s
- RTX 4090: ~35–50 T/s (Mac has slight edge at this size due to bandwidth)
Approximate tokens/second, Llama 3.1 70B Q4_K_M:
- Mac Studio M4 Max 64GB: ~15–22 T/s (fits properly)
- RTX 4090: ~8–12 T/s (at Q2 quantization, degraded quality)
For 70B specifically, the Mac Studio isn't just faster — it's running a fundamentally higher quality model. A Q4 70B is a different experience from a Q2 70B.
Ecosystem and Setup Complexity
Mac Studio:
- macOS — just works. Ollama installs in 5 minutes. Open WebUI runs natively.
- No driver management, no CUDA version conflicts, no Windows vs Linux decision
- Integrated tightly with other Apple devices (iPhone continuity, AirDrop, etc.)
- Updates handled cleanly through macOS
- No noise — the M4 Max Mac Studio is nearly silent under LLM loads
- Power draw: ~120–150W under full inference load
RTX 4090 PC:
- Choice of Windows or Linux — both have tradeoffs
- CUDA drivers, llama.cpp compilation, version dependencies to manage
- Setup time is real: first-time builds take an afternoon to get inference running properly
- Noisier under load — two or three fans running at moderate speed during inference
- More flexible: swap GPUs, add storage, upgrade components independently
- Power draw: 500–700W under full GPU load (5x the Mac)
The PC has more flexibility. The Mac has less friction. Neither is wrong.
Apple Ecosystem Lock-In
This is worth stating clearly: if you buy a Mac Studio, you're in the Apple ecosystem. That's fine if you're already there — seamless for existing Mac users. It's a real cost if you're not.
- You cannot upgrade the memory or GPU. The 64GB you buy is the 64GB you have forever.
- When it's time to upgrade, you sell the whole machine, not just swap the GPU.
- If Apple Silicon inference support for a future framework lags behind CUDA support, you're waiting.
- macOS doesn't give you access to some Linux-first tools that the open-source AI community produces.
Resale value: Apple Silicon Macs hold resale value better than PC GPUs. A Mac Studio tends to sell for 60–70% of original price after two years. An RTX 4090 will depreciate faster as newer GPU generations arrive, though the VRAM content (24GB) keeps it relevant longer than most other PC components.
Who Should Pick the Mac Studio
- You're already in the Apple ecosystem and don't want a second OS to manage
- Running 70B models at quality is important to you
- You value silence and low power draw (home office, no dedicated server room)
- You want something that just works without ongoing maintenance
- Portability between desk and elsewhere matters (Mac Studio is compact, but it's not a laptop)
Who Should Pick the Custom PC
- You want faster inference on 7B–34B models and don't specifically need 70B quality
- You want the ability to upgrade the GPU in 2–3 years without replacing the whole machine
- You prefer Linux for the broader open-source AI tooling ecosystem
- Budget is a constraint and you can build a capable 4090 rig for less than a maxed Mac Studio
- You're already in the PC gaming/workstation ecosystem
The Honest Verdict
Same price range, different strengths. If you need 70B and value simplicity: Mac Studio. If you need maximum speed on mid-size models and want upgrade flexibility: custom PC.
Neither answer is wrong. The mistake is pretending they're equivalent — they're not. Pick based on which tradeoff you can live with.