Strix Halo vs Mac Studio M4 Max for Local AI: Which Unified-Memory Box in 2026?
The GMKtec EVO-X2 (Ryzen AI Max+ 395 "Strix Halo") and the Mac Studio M4 Max both run big models on shared unified memory with no discrete GPU. We compare 128GB LPDDR5X vs up to 192GB Apple unified memory, the software stacks, price, and who should buy which.
Compute Market Team
Our Top Pick
GMKtec EVO-X2 (Ryzen AI Max+ 395)
$1,999 – $3,649Quick Answer
Buy the Mac Studio M4 Max for maximum memory and the MLX ecosystem; buy the Strix Halo box (GMKtec EVO-X2) for a lower entry price and x86 flexibility. The Mac Studio M4 Max ($1,999–$5,999) scales to 192GB of unified memory with a 40-core GPU and Apple's mature MLX/llama.cpp toolchain. The GMKtec EVO-X2 ($1,999–$3,649) pairs a Ryzen AI Max+ 395 (40-CU Radeon 8060S iGPU, 50 TOPS NPU) with up to 128GB of unified LPDDR5X in a Windows/Linux box you can buy on Amazon. Both run large models no discrete 16–32GB card can fit — neither runs CUDA.
Unified memory is the 2026 story for local AI on a desktop. Instead of a discrete GPU with a fixed 16GB or 32GB of VRAM, both the GMKtec EVO-X2 — a "Strix Halo" mini PC built on AMD's Ryzen AI Max+ 395 — and the Apple Mac Studio M4 Max put a large pool of memory in front of an integrated GPU. That single design choice is what lets a compact, quiet box load a 70B-class model at all.
The catch is that this is a capacity play, not a speed play. Unified LPDDR5X (Strix Halo) and Apple's on-package unified memory both trail a discrete GPU's GDDR7 on raw bandwidth, so these machines fit big models but generate tokens more slowly than a card that could hold the same model in dedicated VRAM. The real decision between the two comes down to memory ceiling, software stack, and price.
If you're new to this category, our Strix Halo mini PC guide and RTX 5090 vs Mac Studio M4 Max breakdown set the stage for where unified-memory desktops sit against discrete GPUs.
Strix Halo vs Mac Studio M4 Max — Specs at a Glance
Two very different platforms — x86 Windows/Linux vs Apple Silicon macOS — converge on the same idea: a big unified-memory pool feeding an integrated GPU.
| Spec | GMKtec EVO-X2 (Strix Halo) | Mac Studio M4 Max |
|---|---|---|
| Processor | Ryzen AI Max+ 395 (16-core Zen 5) | Apple M4 Max (16-core CPU) |
| Integrated GPU | Radeon 8060S (40 CU, RDNA 3.5) | 40-core Apple GPU |
| Unified Memory | Up to 128GB LPDDR5X | Up to 192GB |
| NPU | XDNA 2, 50 TOPS | Neural Engine (built in) |
| OS / AI stack | Windows / Linux — ROCm, Vulkan | macOS — MLX, llama.cpp, Ollama |
| CUDA | No | No |
| Price | $1,999–$3,649 | $1,999–$5,999 |
Specs and pricing from our product catalog. Configurations and street prices vary.
The Memory Ceiling — 128GB vs 192GB
For unified-memory machines, the memory ceiling is the spec sheet. It sets the largest model you can load and how much room is left for context and concurrency on top of the weights.
- Strix Halo (128GB): Enough to load 70B-class models on an integrated GPU and leave headroom for moderate context. The EVO-X2's 128GB is the config that makes this box interesting for local AI — and it's not user-upgradeable, so buy the memory you need up front.
- Mac Studio M4 Max (up to 192GB): The extra headroom is what pulls ahead — Apple positions 192GB to hold very large mixture-of-experts models at Q4, plus longer context and bigger batches. If your target models push past 128GB, only the Mac fits them in this class.
To size memory to your actual models before you buy, our system RAM guide and VRAM guide are the companion reads — unified memory changes the math but not the goal of fitting weights plus context.
Software: MLX Maturity vs ROCm/Vulkan
Neither machine runs CUDA, so the question is which non-CUDA stack you're comfortable in. Apple's MLX plus llama.cpp and Ollama is well-trodden on Apple Silicon — most popular local-AI tooling has first-class Mac support. On the Strix Halo, you're on ROCm or Vulkan back-ends, which run the mainstream inference engines but are less mature and occasionally need more setup. Our MLX vs llama.cpp guide covers the Apple side, and the Ryzen AI Max review digs into the AMD software experience.
Price and Platform — x86 vs Apple
Both start around $1,999, but they diverge from there. The EVO-X2 is a Windows/Linux x86 machine you can buy on Amazon — useful if you want the broad software compatibility of PC or plan to dual-boot Linux for inference. Note that Amazon third-party listings for the 128GB config carry a steep markup over GMKtec direct, so check both. The Mac Studio climbs to ~$5,999 for its largest memory and storage, buying you silent macOS operation and the Apple ecosystem, but no expandability after purchase.
Need CUDA? Consider the DGX Spark Instead
If your work depends on CUDA-only libraries, neither of these boxes fits — but there's a third unified-memory option that does. NVIDIA's DGX Spark ($3,999) pairs a GB10 Grace Blackwell superchip with 128GB of coherent unified memory and runs the full CUDA stack, making it a CUDA-native alternative to a Strix Halo box for large-model prototyping. It costs more than the EVO-X2 and trades the Mac's memory ceiling for CUDA compatibility. See our DGX Spark vs Strix Halo and DGX Spark vs Mac Studio M4 Max breakdowns for the full three-way picture.
The Verdict — Who Should Buy Which
Buy the Mac Studio M4 Max if you want the largest memory pool in this class (up to 192GB), silent macOS operation, and the polished MLX toolchain — and you're willing to pay up for the top configs. It's the pick for the biggest models and long-context work.
Buy the GMKtec EVO-X2 (Strix Halo) if 128GB covers your models, you want to stay on Windows/Linux, you value the 50 TOPS NPU and x86 compatibility, or you simply want to spend less to get into the unified-memory tier. It's the better value for most people entering this category.
Still weighing your options? Compare either against a discrete flagship in our RTX 5090 vs Mac Studio M4 Max breakdown, or see where these boxes rank in our best hardware for AI guide.
Frequently Asked Questions
Strix Halo or Mac Studio M4 Max — which is better for running large local LLMs?
Both put a large pool of unified memory in front of an integrated GPU, so both can load models a 16GB or 24GB discrete card can't. The Mac Studio M4 Max scales higher — up to 192GB of unified memory versus 128GB on the Strix Halo (GMKtec EVO-X2) — so the very largest models and long-context workloads favor the Mac. The Strix Halo box starts far cheaper — from $1,999 direct from GMKtec for a Windows/Linux x86 machine (Amazon listings for the 128GB config run higher) — and adds a 50 TOPS NPU. Pick the Mac for maximum memory and the MLX software ecosystem; pick the Strix Halo for a lower entry price and x86 flexibility.
How much memory does the GMKtec EVO-X2 (Ryzen AI Max+ 395) have, and what can it run?
The EVO-X2 tops out at 128GB of unified LPDDR5X shared between its 16-core Zen 5 CPU and 40-CU Radeon 8060S iGPU. That's enough shared memory to load 70B-class models on an integrated GPU — something no consumer discrete card with 16–32GB can do without offloading. The trade-off is bandwidth: unified LPDDR5X moves data slower than a discrete GPU's GDDR7, so large models load but generate tokens more slowly than they would on a card that could actually fit them.
Does either machine support CUDA?
No. Neither the Strix Halo box nor the Mac Studio M4 Max has an NVIDIA GPU, so neither runs CUDA. On the Mac you use MLX, llama.cpp, and Ollama, which are mature on Apple Silicon. On the Strix Halo you use ROCm or Vulkan back-ends, which work but are less mature than CUDA or MLX. If your workflow depends on CUDA-only libraries, you want an NVIDIA path instead — see our DGX Spark comparison for a CUDA-native 128GB alternative.
Is the Mac Studio M4 Max worth the extra money over a Strix Halo mini PC?
It depends on the config you need. Both start near $1,999, but a Mac Studio scales to 192GB of unified memory (with pricing up to ~$5,999), while the EVO-X2 caps at 128GB. If you need the absolute largest memory pool, silent macOS operation, and the polished MLX toolchain, the Mac earns its premium. If 128GB covers your models and you'd rather stay on Windows/Linux, keep a discrete-GPU upgrade path in the ecosystem, or spend less, the Strix Halo box is the better value.
Which is faster for token generation — Strix Halo or Mac Studio M4 Max?
Both are limited by unified-memory bandwidth rather than raw compute, and on models that fit either machine, throughput comes down to each platform's memory bandwidth and how well-tuned its runtime is (MLX on the Mac, ROCm or Vulkan on the Strix Halo). We don't publish head-to-head token-per-second numbers for these two, so benchmark your target model on the config you're considering rather than trusting a single figure. What's certain either way: neither matches a discrete GPU's speed on models small enough to fit that GPU's VRAM — their advantage is capacity, not peak throughput.