Unified Memory Showdown: The Cheapest Mac Mini Is Actually the Best Fit for Most People

Unified memory is the real ticket to local AI.

Running local large models has one hard requirement: VRAM.

A 30B-parameter model, quantized with Q4_K_M, needs roughly 18-20GB. A 70B model jumps straight past 40GB. The VRAM ceiling on consumer GPUs is what it is—the RTX 4090 tops out at just

💡 What You Will Learn

Unified Memory Showdown: The Cheapest Mac Mini Is Actually the Best Fit for Most People Unified memory is the real ticket to local AI. Running local large models has one hard requirement: VRAM. A 3

📜 Table of Contents

• Unified Memory Is the Ticket to Local AI
• Where the Price Difference Comes From
• The Metrics That Actually Separate Them
• The Truth About Value
• The Variable You're Most Likely to Overlook
• One-Sentence Summary

The Three Unified Memory Giants Compared: The Cheapest Mac Mini Is Actually Best for Most People

Unified Memory Is the Ticket to Local AI

Running local large models has one hard bottleneck: VRAM.

A 30B-parameter model, quantized with Q4_K_M, needs roughly 18-20GB. A 70B model jumps straight past 40GB. Consumer GPUs hit their VRAM ceiling fast—the RTX 4090 tops out at 24GB, so running 70B means splitting layers across passes, and speed gets cut in half.

Unified memory breaks that limit. The CPU and GPU share the same memory pool—whatever the model needs, you give it. No copying data back and forth, no worrying about VRAM overflow.

Right now, there are only three unified-memory AI machines you can actually buy:

• Mac Mini M4 Pro — Apple Unified Memory, up to 64GB
• AMD Strix Halo — AMD's unified memory solution, up to 128GB
• DGX Spark — NVIDIA Grace Blackwell, up to 128GB

Three machines, three architectures, three price tiers. But when you actually open your wallet, the truth behind the numbers is far more complicated than it looks.

Where the Price Difference Comes From

Let's start with pricing:

Looking purely at memory value, the Strix Halo's 128GB at $1,500 costs about half per GB of the Mac and a third of the DGX.

But memory is just the storage medium—what you're really paying for is how fast it runs.

The Metrics That Actually Separate Them

I ran the same model (Qwen3-Coder 30B, Q4_K_M) on the same backend (llama.cpp/Ollama) across all three machines under identical conditions.

The results were surprising:

Generation speed — the column where you watch text appear word by word:

Mac Mini at 56 t/s, Strix Halo at 73 t/s, DGX at 84 t/s. The gap between the most expensive and the cheapest is just 28 tokens per second. You read Chinese at roughly 20-30 characters per second, which translates to about 15-20 t/s. All three machines generate faster than you can read—the chat experience is virtually indistinguishable.

Prefill speed — how fast the model ingests your input context:

This is the real dividing line. The DGX's 2,107 t/s is 3.7x the Mac and 6x the Strix Halo. If you're processing long documents, heavy RAG chunks, or complex agent contexts, the DGX's prefill advantage is overwhelming.

But here's the question: what most people actually need is generation, not prefill.

The Truth About Value

If you're just chatting, writing, or fixing code, 90% of your workload lives in the generation speed column. In that column, the Mac Mini's 56 t/s versus the DGX's 84 t/s is a negligible difference in experience—but the price gap is 7x.

What about the Strix Halo? 128GB for $1,500, with generation at 73 t/s—even faster than the Mac. It looks like the sweet spot. But ROCm compatibility is a real liability—much of the local AI ecosystem only supports CUDA. Hit one unsupported tool, and your 128GB of unified memory is stuck at "compiling."

So the value king for each scenario is actually:

Chatting, writing, short prompts → Mac Mini M4 Pro

The experience you get for $600 is nearly identical to the $4,700 DGX. The $4,000 you save could buy a solid gaming laptop plus a few months of cloud GPU rental for heavy lifting. And Mac's MPS support in the local AI ecosystem is already mature—the tools you need are basically all there, at just 20W power draw, silent, and maintenance-free.

Large context, RAG, long agent workflows → DGX Spark

Is $4,700 worth it? Depends on whether you need it. If you're doing RAG retrieval across dozens of documents daily, or running long-context agent workflows, the DGX's prefill advantage is something the Mac simply can't match. In those scenarios, the DGX has no competition.

Running models AND gaming AND Windows → Strix Halo

128GB for $1,500 is genuinely tempting. But you have to accept the ROCm lottery—hit a CUDA-only tool and your experience falls off a cliff. For tinkerers willing to put in the work, the Strix Halo is the hardware with the most potential, but it's also the one that demands the most patience.

The Variable You're Most Likely to Overlook

How big a model your unified memory can run comes down to how much memory you have.

A 30B model needs 18-20GB, a 70B needs 40GB+. If open-source models hit 100B+ in the future, the Mac's 64GB might not cut it anymore.

The Strix Halo's 128GB and the DGX's 128GB are the real advantage here—you can run not just today's models but also what comes out in the next year or two. The Mac's 64GB ceiling means you're capping your future potential the moment you buy.

But flip it around: does the average person really need to run a 70B model? A 30B model is already very capable at reasoning, and 70B inference on unified memory will be noticeably slower than 30B. Paying for "maybe someday" versus paying for "what I actually need right now" are two very different value calculations.

One-Sentence Summary

Unified memory for local LLMs isn't a single value curve—it's three:

• Mac Mini: Best experience today, best price, but low memory ceiling
• Strix Halo: Insane memory value, but ROCm compatibility will scare off beginners
• DGX Spark: Crushes prefill, but 90% of users won't use that advantage—$4,700 is massive overkill

Ask yourself three questions first: What model size are you running? Does your workload eat generation or prefill? Are you willing to wrestle with ecosystem compatibility?

Once you have those answers, the value proposition sorts itself out.