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

Running Large Language Models (LLMs) locally is less about raw compute and more about one thing: Memory.

If you want to run the "smart" models—the ones that don't just summarize emails but actually reason, code, and understand nuance—you need memory. A lot of it.

I've spent months optimizing my personal setup to run models like GLM-4.5, GLM-4.5-air, Qwen-3 235B, and Qwen-3-next-80B, and the hardware landscape is more nuanced than it appears. Here's my breakdown of the costs, the bottlenecks, and why I ultimately settled on a Mac Studio with 256GB unified memory.

The Real Bottleneck: Memory Bandwidth

When running LLMs, the speed limit isn't your GPU core clock—it's Memory Bandwidth. The model shuffles massive weight matrices from memory to compute units for every single token generated. This is why a gaming GPU with high FLOPS but low memory bandwidth will disappoint you.

Here's my theory on memory capacity tiers:

• 96GB: Bare minimum. You can run quantized 70B models, but you're limited.
• 128GB: Good starting point. Opens up larger quantizations and some 100B+ models.
• 256GB: Top tier. This is where you access frontier-class intelligence locally—models like Qwen-3 235B with decent quantization.
• 512GB: Diminishing returns. While tempting for massive models like Kimi k2 or DeepSeek v3, memory bandwidth plateaus. Inference speeds drop drastically, making the price-to-performance ratio unattractive.

Why I Chose the Mac Studio (256GB)

I went with the Mac Studio M2 Ultra with 256GB of Unified Memory (~$6,500).

For personal and family use, it's unbeatable. The Unified Memory Architecture gives the GPU direct access to the entire 256GB pool—no fragmentation, no copying between CPU and GPU memory. To match this VRAM on the NVIDIA side, you're building a loud, power-hungry multi-GPU cluster with its own set of bottlenecks.

The Caveats: Metal vs. CUDA

It's not all sunshine. NVIDIA's CUDA ecosystem is still the gold standard for developing and training ML models. If you're writing custom kernels or fine-tuning models, CUDA has a decade head start with mature libraries and optimization tools.

Apple's Metal is catching up fast. The mlx-community is doing heroic work—nearly every major open-source model now has a Metal-optimized version. But if you're building novel architectures, you'll find more CUDA optimization examples than Metal ones.

However, for inference and tool calling, Metal performance is fantastic.

My Favorite Models

These models run exceptionally well on my Mac Studio and are my daily drivers:

• GLM-4.5 / GLM-4.5-air: Excellent general knowledge with surprisingly strong coding performance. Great for tool calling.
• Qwen-3 235B (quantized): The heavyweight. Frontier-class reasoning when you need it.
• Qwen-3-next-80B: Perfect balance of speed and intelligence. My go-to for most tasks.

All these models excel at tool calling and general knowledge, which is critical for agentic workflows.

The Competition: NVIDIA & Ryzen

The NVIDIA Route (RTX 3090/4090/6000 Ada)

If you need raw throughput for multiple concurrent users, NVIDIA wins. Individual cards have higher memory bandwidth than Mac Studio, and CUDA optimizations are unmatched.

The Setup:

• 4x RTX 3090 (Used): ~$3,500 for 96GB total VRAM (24GB each). Each card: 936 GB/s bandwidth, 350W TDP.
• 2x RTX 4090: ~$3,200 for 48GB total VRAM (24GB each). Each card: 1,008 GB/s bandwidth.
• 2x RTX 6000 Ada: ~$14,000 for 96GB total VRAM (48GB each). Professional-grade, 960 GB/s per card.

The Bottleneck Reality: VRAM is fragmented across cards. To run a single large model that doesn't fit on one GPU, you must split it across multiple GPUs.

RTX 3090: No NVLink. Multi-GPU communication limited to PCIe 4.0 x16 at ~32 GB/s per direction. This is only 3.4% of the card's internal 936 GB/s memory bandwidth. When model layers span GPUs, you're bottlenecked hard.

RTX 4090: Same story—no NVLink, PCIe only. Great for single-GPU workloads, limited for distributed inference.

RTX 6000 Ada: Has NVLink support at ~450-900 GB/s bidirectional. Much better for multi-GPU setups, but you're paying $7,000+ per card.

When to Choose NVIDIA:

• Running a small firm with multiple concurrent users
• Serving many smaller models in parallel (each on its own GPU)
• Models that fit comfortably on a single 24GB or 48GB card
• Need CUDA ecosystem for training/fine-tuning

When NOT to Choose Multi-GPU NVIDIA:

• Running a single massive model for personal use (PCIe bottleneck kills you)
• Budget-conscious and need unified large memory pool

The "DigiSpark" & Ryzen AI 300

There's buzz around the NVIDIA DigiSpark (unreleased at time of writing)—128GB memory for ~$4,000. The capacity looks attractive, but reported memory bandwidth of ~273 GB/s is the dealbreaker. That's too slow for responsive inference on large models.

Similarly, the Ryzen AI 300 series is dirt cheap with shared system memory, but ~256 GB/s bandwidth gets you nowhere. You'll be waiting frustratingly long for responses.

The lesson: Memory capacity without bandwidth is like having a massive hard drive with a USB 2.0 connection.

The 512GB Trap

The 512GB Mac Studio costs nearly double the 256GB version (~$9,000 vs ~$6,500). Tempting for massive models like Kimi k2 or DeepSeek v3, right?

Don't fall for it.

The M2 Ultra's memory bandwidth (~800 GB/s) doesn't scale with capacity. At 512GB, bandwidth becomes a hard ceiling. You can load these massive models, but inference speeds drop drastically—the chip can't move data fast enough relative to model size. The price-to-performance ratio falls off a cliff.

256GB is the sweet spot for Mac Studio. Beyond that, look at distributed systems.

My Verdict

For personal/family use: Mac Studio 256GB is unbeatable. Quiet, efficient, and powerful enough to run frontier models locally.

For small firms: Build an NVIDIA RTX cluster (multiple 4090s or 6000 Adas). You get higher total throughput for serving multiple users or running many smaller models in parallel.

Why I'm Optimistic About My Setup

With MoE (Mixture of Experts) architectures becoming standard, the Mac's unified memory architecture is perfectly positioned. MoE models keep most parameters dormant and only activate relevant "experts" per token. This means:

• Lower active memory pressure
• Efficient parameter switching without GPU-to-GPU copying
• Better utilization of large memory pools

Models like Qwen-3 and GLM-4.5 already use MoE variants, and this trend is accelerating.

Hardware Comparison Table

Real-World Performance (Mac Studio 256GB)

Here are actual inference speeds I'm seeing with my setup:

• Qwen-3 235B (Q4 quantization): ~30 tokens/sec — Impressively fast for a 235B parameter model
• GLM-4.5: ~25 tokens/sec — Smooth and capable for complex reasoning
• GLM-4.5-air: ~53 tokens/sec — Extremely responsive, near-instant feel
• Qwen-3-next-80B: ~70 tokens/sec — Lightning fast, my daily driver, perfect balance

These speeds are for actual inference with tool calling and complex prompts, not synthetic benchmarks. The performance is significantly better than expected, making these models genuinely practical for interactive, production use.

Software: How to Actually Run These Models

For running models, I use LM Studio (ships Metal-optimized models fastest, simple interface) and Open WebUI (adds multi-user auth and customizations, perfect for family/team access or small firms). Other solid options include Ollama (CLI-first, great for scripting) and llama.cpp (maximum control, what most tools use under the hood). The mlx-community ensures Mac users get Metal-optimized releases within days of any major model drop.

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Bottom line: For running frontier LLMs locally in 2025, memory capacity and bandwidth matter more than GPU cores. The Mac Studio 256GB hits the sweet spot for personal use, while NVIDIA clusters make sense when you need to serve multiple users. Choose based on your throughput needs, not just model size ambitions.