Local LLM Hardware Guide 2025: Mac Studio vs. NVIDIA & Ryzen

TL;DR

For running large LLMs locally, memory bandwidth is the real bottleneck—not GPU cores. Mac Studio with 256GB unified memory offers the best price-to-performance for models like Qwen-3 235B, while NVIDIA shines for batched inference and production workloads.

Look, if you want to run LLMs locally, forget about GPU cores for a minute. It all comes down to memory and memory bandwidth.

I'm not talking about running some toy chatbot that just repeats Wikipedia. I mean the models that actually think. The ones that can reason through hard problems, write working code, follow complex instructions, and build agents that call tools. Those need serious memory. Way more than most people realize.

I've been working on my local AI setup for months now. Running GLM-4.5, Qwen-3 235B, and a few other big models. Building agents that can browse the web, call APIs, and follow system prompts. After spending way too many hours reading specs and running benchmarks, I ended up with a Mac Studio with 256GB unified memory. Let me tell you why, and when NVIDIA still makes more sense.

It's All About Memory Bandwidth

Here's what took me a while to understand: the bottleneck is not the GPU clock speed. It's memory bandwidth. Every time the model outputs a token, it moves huge weight matrices from memory to compute units. A gaming GPU with great FLOPS but slow memory? It won't work well.

My rough guide for memory capacity:

• 96GB: Bare minimum. You can fit quantized 70B models, but you run out of room fast.
• 128GB: Good starting point. Gets you into larger quantizations and some 100B+ models.
• 256GB: This is the sweet spot. Big models like Qwen-3 235B become usable. You can run real agents with tool calling.
• 512GB: Not worth it. Yes, you could load Kimi k2 or DeepSeek v3, but the bandwidth is the same as 256GB. Inference speed does not get faster. You just pay more to load bigger models that run at the same speed.

Why I Went with Mac Studio

I got the Mac Studio M2 Ultra with 256GB. Cost me about $5,600.

For what I need, it works great. Personal projects, family use, demos, building AI agents that call tools, testing browser automation. The unified memory is not just marketing. The GPU gets direct access to all 256GB. No copying data between CPU and GPU memory. No fragmentation problems.

To get the same VRAM on NVIDIA, you need a multi-GPU setup that is loud and uses a lot of power. Not for me.

The CUDA Problem (But Metal Is Good Now)

I won't say it's perfect. If you train models from scratch or write custom CUDA code, NVIDIA is still ahead. The tools are mature. The community is big. You can find answers online.

But here's what's changed. The mlx-community has done great work. Almost every big open-source model now has a Metal version, usually within a week of release. For inference and tool calling, which is most of what I do, Metal works well.

Apple has also been making progress on their own. They now have MLX, which is like PyTorch but for Metal. The framework is getting better fast. Writing custom layers and training small models on Mac is now possible. It's not at CUDA level yet, but the gap is closing.

For running agents that follow instructions, call tools, and work with system prompts, the Mac setup is solid.

What I Actually Run

My daily models:

• GLM-4.5 and GLM-4.5-air: Good all-around models. Strong at coding. Tool calling works well. GLM-4.6 is out now too.
• Qwen-3 235B (quantized): The heavy one. When I need serious reasoning, this is it.
• Qwen-3-next-80B: My main model. Fast enough to feel responsive, smart enough for most agent tasks.

All these models are good at following instructions and calling tools. That matters for building agents that actually work.

What About NVIDIA?

The GPU Cluster Route

If you serve multiple users or need high throughput, NVIDIA still wins. Per-card bandwidth is higher than Mac, and CUDA is well optimized.

Some numbers:

• 4x RTX 3090 (used): About $3,500 for 96GB total VRAM. Each card does 936 GB/s bandwidth but uses 350W power.
• 2x RTX 4090: About $3,200 for 48GB total. Faster at 1,008 GB/s per card.
• 2x RTX 6000 Ada: $14,000 for 96GB. Professional grade, 960 GB/s each.

The problem is that VRAM is split across cards. If you want to run one big model, you have to split it across GPUs. Then you hit interconnect bottlenecks.

The 3090 has no NVLink. Multi-GPU communication is limited to PCIe 4.0 speeds, about 32 GB/s. That's only 3.4% of the card's memory bandwidth. When model layers span cards, it slows down a lot.

The 4090 has the same problem. The 6000 Ada has NVLink at 450-900 GB/s, which helps. But you pay $7,000+ per card.

NVIDIA makes sense when:

• You run a small company with multiple people using the models
• You want to serve several smaller models at the same time
• Everything fits on a single 24GB or 48GB card
• You need CUDA for training or fine-tuning

Skip multi-GPU NVIDIA when:

• You are one person running one big model (PCIe bottleneck will slow you down)
• You need a large unified memory pool without the complexity

DigiSpark and Ryzen AI 300

I've been watching the NVIDIA DigiSpark. 128GB for around $4,000 sounds good. But the bandwidth is only about 273 GB/s. Too slow for good inference.

Ryzen AI 300 is similar. Cheap, yes, but 256 GB/s bandwidth with shared system memory is not enough. You will wait a long time for responses.

Think of it this way: memory without bandwidth is like a big hard drive connected with USB 2.0. Big storage, but slow to use.

The Bottom Line

Personal or family use? Mac Studio 256GB. Quiet, low power, runs big models without problems. Good for building personal agents.

Small team or business? Build an NVIDIA cluster with 4090s or 6000 Adas. More throughput for multiple users.

Why I'm Not Worried Long Term

MoE (Mixture of Experts) models are becoming common. Models like Qwen-3 and GLM-4.5 use this design. MoE keeps most parameters inactive and only uses the relevant "experts" for each token. This works well with Mac's unified memory. Less data moving between GPUs. Better use of the large memory pool.

For agent workloads where you need good instruction following and tool calling, this setup is solid.

Quick Reference

Device	Memory	Price	Bandwidth	Best For
Mac Studio M2 Ultra	256GB	~$5,600	~800 GB/s	Personal use, agents
Mac Studio M2 Ultra	512GB	~$9,400	~800 GB/s	Not worth it
4x RTX 3090 (used)	96GB	~$3,500	~936 GB/s/card	Multi-model serving
2x RTX 4090	48GB	~$3,200	~1,008 GB/s/card	Smaller models
2x RTX 6000 Ada	96GB	~$14,000	~960 GB/s/card	Small business
NVIDIA DigiSpark	128GB	~$4,000	~273 GB/s	Skip it
Ryzen AI 300	Shared	<$2,000	~256 GB/s	Learning only

What I'm Actually Getting

Real numbers from my Mac Studio 256GB:

• Qwen-3 235B (Q4): About 30 tokens/sec. Surprisingly usable for a 235B model.
• GLM-4.5: About 25 tokens/sec. Smooth for complex reasoning and tool calling.
• GLM-4.5-air: About 53 tokens/sec. Feels almost instant.
• Qwen-3-next-80B: About 70 tokens/sec. This is my daily driver. Good balance of speed and smarts.

These are real numbers with tool calling and actual prompts. Not synthetic benchmarks made for marketing.

Software Stack

I mostly use LM Studio because Metal models show up there fast and it's simple to use. For sharing with family, Open WebUI adds multi-user login and works well.

Ollama is good if you like command line or want to script things. llama.cpp gives you the most control. Most tools use it under the hood anyway.

The mlx-community ships Metal versions within days of any major model release. Mac users are in a good spot now.

For agent work, I use these models with tool calling enabled. They follow system prompts well and can handle multi-step tasks.

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Memory capacity and bandwidth beat GPU cores for local LLM work. The Mac Studio 256GB is the sweet spot for personal use and building agents. NVIDIA makes sense when you serve multiple users. Pick based on your actual needs, not just the biggest numbers.

Thoughts? Hit me up at [email protected]