For three years Apple Silicon had a monopoly on consumer “lots of unified memory” — the only way to address 64+ GB of memory from both CPU and GPU at once. AMD’s Ryzen AI Max+ 395 (Strix Halo) changed that in 2026 with up to 128 GB of unified memory in laptops costing under $3,000.
But Apple’s M4 Pro (48 GB max) isn’t standing still. Here’s the honest matchup.
Key takeaways
- Strix Halo wins on memory ceiling: 128 GB vs 48 GB max — almost 3×.
- M4 Pro wins on efficiency: half the power draw, longer battery, quieter.
- For 30B-70B LLMs: Strix Halo unlocks models the M4 Pro can’t hold.
- For 8B-30B LLMs: M4 Pro is more elegant — same speed, better battery.
- Software: MLX (Apple) is more mature than ROCm on Strix Halo today.
What you’re actually buying
| Spec | Ryzen AI Max+ 395 (Strix Halo) | Apple M4 Pro |
|---|---|---|
| CPU cores | 16 Zen 5 | 14 (10P + 4E) |
| GPU | Radeon 8060S (40 RDNA 3.5 CUs) | 16-core Apple GPU |
| NPU | 50 TOPS XDNA 2 | 38 TOPS (M4 Pro) |
| Max unified memory | 128 GB LPDDR5X-8000 | 48 GB LPDDR5X-8533 |
| Memory bandwidth | 256 GB/s | 273 GB/s |
| TDP | 120 W | ~55 W |
| Laptops available | HP ZBook Ultra G1a, Framework Desktop, Asus ProArt P16 | MacBook Pro 14″/16″, Mac mini Pro |
| Price (128 GB / 48 GB) | ~$2,800 (128 GB Strix Halo laptop) | $2,799 (48 GB MacBook Pro 14″) |
The configurations match prices: $2,800 gets you either machine with the most unified memory of its kind.
What the memory numbers mean for AI speed
Both chips feed the GPU from unified memory, so for large language models the limiting factor is how fast the weights can be read out of memory. Dividing memory bandwidth by the gigabytes read per token gives an upper bound on generation speed. These are arithmetic ceilings from the published bandwidth figures in the table above, not measured results; real-world speeds come in lower.
| Model at 4-bit (approx. weights) | Strix Halo (256 GB/s, 128 GB) | M4 Pro (273 GB/s, 48 GB) |
|---|---|---|
| 8B (~5 GB) | ~51 tokens/s ceiling | ~55 tokens/s ceiling |
| 14B (~8.5 GB) | ~30 tokens/s ceiling | ~32 tokens/s ceiling |
| 32B (~18 GB) | ~14 tokens/s ceiling | ~15 tokens/s ceiling |
| 70B (~40 GB) | ~6 tokens/s ceiling | At the memory limit; macOS reserves part of unified memory, so it does not fit comfortably |
| 120B-class (~65 GB) | Fits | Does not fit |
The pattern: for models that fit in both, the two chips have almost the same speed ceiling, with the M4 Pro slightly ahead on bandwidth. Above roughly 30B parameters, Strix Halo wins on what is possible, because the M4 Pro tops out at 48 GB. Our VRAM calculator works out the memory a specific model and context length needs.
Image generation (SDXL, FLUX) is limited more by GPU compute than by bandwidth, and results vary widely with the software stack: MLX and Core ML on the Mac, ROCm on AMD.
Where Strix Halo shines
The killer feature is the 128 GB ceiling. For AI builders who care about running larger models locally without leaving the laptop form factor, this is the only consumer option. M4 Max in the MacBook Pro 16″ also goes to 128 GB, but it costs $4,999 — Strix Halo gives you the same memory ceiling at $2,800.
Also strong on Strix Halo:
- Windows + Linux flexibility — works with the broader CUDA-adjacent toolset (excluding actual CUDA)
- More CPU cores for parallel workflows
- Better gaming (RDNA 3.5 outperforms Apple GPU on game workloads)
- Cheaper per-GB-of-memory at the 128 GB tier
Where M4 Pro wins
- Battery life: Apple silicon MacBooks generally lead Windows AI laptops in independent battery reviews
- Build quality: MacBook Pro is in a class by itself for build precision
- Software maturity: MLX has been shipping for 2 years; ROCm on Strix Halo is newer
- Screen: 14″ Mini-LED, 1600 nits, P3 — best laptop display
- Efficiency: a ~55 W chip against 120 W means less heat and fan noise under sustained load
- Slightly higher memory bandwidth (273 vs 256 GB/s) for models that fit in both
Pros and cons
Strix Halo (Ryzen AI Max+ 395)
- Cheapest 128 GB unified memory laptop
- Strong Windows + Linux flexibility
- Better gaming performance
- 16 CPU cores for parallel work
Strix Halo limits
- Newer ecosystem (ROCm + Strix Halo combo still maturing)
- 120 W TDP — louder, hotter, shorter battery
- Fewer top-tier laptop options
- Software gaps vs MLX
Apple M4 Pro
- Best per-token speed for models that fit
- Excellent battery during AI inference
- Mature MLX/Metal ecosystem
- Best laptop build + display
M4 Pro limits
- 48 GB memory ceiling
- Locked into macOS
- $2,799 starting (matches Strix Halo without 128 GB)
- For 128 GB you need M4 Max ($4,999)
The decision
- Run 70B+ LLMs locally on a laptop, budget $2,800: Strix Halo wins by default. Nothing else fits.
- Inference up to 30B + want best laptop experience: M4 Pro. Better build, longer battery, faster per-token in your model range.
- Need Windows + AI on a laptop: Strix Halo (only credible option).
- Need >48 GB on Apple: Step up to MacBook Pro M4 Max 128 GB at $4,999.
See our best laptops for ML guide for the full ranking.
Which large models actually fit
The single most important number in this matchup is memory. The Ryzen AI Max+ 395’s 128 GB of unified memory (with roughly 100 GB+ addressable by the GPU) can load 70B and even ~120B-class models — dense and MoE alike, including Llama 4 and DeepSeek variants — that simply will not fit on the Apple M4 Pro’s 48 GB.
The trade-off is raw speed. Strix Halo is compute-bound, not memory-bound: it runs roughly 3–4× slower than an RTX 4090 for image generation, and on small 8B models a 4090 pushes ~127 tokens/sec to Strix Halo’s ~48. Against Apple, though, it pulls ahead where it counts for creators — in Stable Diffusion 3.5 it posts about 3.9× the Mac’s speed. The summary: Strix Halo wins decisively on what fits; the M4 Pro stays competitive only on smaller models and on efficiency.
The software tax: how much tinkering each one really needs
Benchmarks assume both machines are already running at full tilt. Getting there is a very different story, and for many buyers the day-one experience matters more than a 15% gap in tokens per second. This is the dimension where the two platforms diverge hardest.
On the M4 Pro, local inference is close to plug-and-play. Install Ollama or LM Studio, pull a model, and you have an OpenAI-compatible endpoint on localhost:11434 in minutes. Apple’s MLX framework and the Metal backend in llama.cpp are mature and stable, so quantized models “just work” with no driver hunting, no environment variables, and no kernel modules to wrangle. You trade flexibility for the fact that nothing fights you.
Strix Halo rewards patience. The chip’s iGPU (gfx1151) is still marked Preview in AMD’s ROCm stack as of early 2026, which means the smoothest path is often not ROCm at all. The community consensus is that the Vulkan (RADV) backend in llama.cpp frequently beats AMD’s own ROCm on this hardware at normal context lengths, and Vulkan is far easier to stand up: install Mesa drivers and go. If you want ROCm specifically, expect to set HSA_OVERRIDE_GFX_VERSION=11.5.1 and lean on community nightly builds rather than the stock release. ROCm tends to pull ahead on heavy prompt processing and very long context windows, so RAG-heavy users may want it despite the friction.
Two practical implications:
- Pick your OS deliberately. Strix Halo is happiest on Linux. Windows support exists but the LLM tooling lags, so a Windows-only buyer loses part of the chip’s advantage.
- Budget setup time, not just money. Plan on an afternoon of configuration for Strix Halo versus roughly fifteen minutes on the Mac.
The honest framing: if your time is worth more than the price gap, the M4 Pro’s frictionless stack is a real feature. If you enjoy owning the full stack and want maximum capacity per dollar, Strix Halo’s rougher edges are a fair trade once it is dialed in.
Verify these figures at the source
Specifications and prices change without notice. These are the vendors’ own pages for what is covered above:
FAQ
Is Strix Halo’s 128 GB actually usable as VRAM?
Yes. Like Apple’s unified memory, the entire 128 GB pool is addressable by the GPU. AMD’s drivers (in 2026) allow allocating up to 96 GB to the GPU explicitly. Llama 3 70B at Q5 (50 GB) fits comfortably.
Does ROCm work on Strix Halo?
Yes, as of ROCm 6.3+. PyTorch, llama.cpp, Stable Diffusion all run. Not as polished as CUDA or as mature as MLX, but production-viable. See our ROCm vs CUDA 2026 deep dive.
Why isn’t Strix Halo cheaper since it’s “just” a Ryzen chip?
The 128 GB LPDDR5X-8000 alone is ~$600 of memory. Plus the larger die with the Radeon 8060S iGPU and 50 TOPS NPU. The chip itself is premium silicon — you’re paying for the die size, not just the brand.
Will there be a Strix Halo successor in 2027?
AMD has confirmed continued investment in the AI Max+ platform with successors planned for 2027. Don’t wait if you have a workload now — 2027 timelines on AMD have historically slipped.
Snapdragon X Elite — is it a competitor?
Different category. Snapdragon X Elite is 16 GB max LPDDR5X, no discrete GPU equivalent, no PyTorch CUDA path. It’s a thin-and-light laptop chip; Strix Halo is a mobile workstation chip. They don’t really compete on AI workloads beyond 8B models. See our Snapdragon X Elite vs M4 comparison.
Can the Ryzen AI Max+ 395 run a 70B model?
Yes. Its 128 GB of unified memory (about 100 GB+ available to the GPU) loads 70B models and larger MoE architectures locally — something the 48 GB M4 Pro cannot do without heavy quantization or falling back to the cloud.
Is Strix Halo faster than an RTX 4090 for AI?
No. It’s compute-bound — roughly 3–4× slower for image generation and about 48 vs 127 tokens/sec on 8B models. Its advantage over a discrete GPU is capacity (running models that don’t fit in 24 GB of VRAM), not speed.
Strix Halo or M4 Pro for Stable Diffusion?
Strix Halo — it runs roughly 3.9× the M-series Mac’s Stable Diffusion 3.5 speed. For LLM-primary work the memory capacity matters even more; only buy the discrete-GPU route if image generation is your main, latency-sensitive workload.
Which is better for an always-on local LLM server at home?
Either works, but they optimize differently. Strix Halo mini PCs give you the most memory for a 24/7 box and run a standard Linux server stack, but in a high-performance configuration the APU can pull well over 100W under sustained load and the small chassis fans are audible when busy. An M4 Pro Mac mini idles in the single-digit watts and stays near-silent, which suits a machine that lives on a desk, though its memory ceiling caps how large a model you can keep resident. For maximum model size, pick Strix Halo; for a quiet, low-idle appliance, pick the Mac.
Can I get an M4 Pro Mac mini with 64GB of RAM?
No. As of 2026 the M4 Pro Mac mini tops out at 48GB of unified memory; the 64GB configuration is only available on the MacBook Pro. That ceiling matters here because this comparison is largely about fitting big models in memory, and 48GB meaningfully limits which quantized models stay resident versus Strix Halo’s 128GB. If you need 64GB-plus in a desktop, you are looking at a Mac Studio or a Strix Halo box, not the Mac mini.
Do both machines expose an OpenAI-compatible API for my apps?
Yes, and that is the practical equalizer. Ollama, LM Studio, and llama.cpp’s server all serve an OpenAI-style endpoint on both platforms, so existing code that points at the Chat Completions API generally works unchanged against either machine. The difference is upstream: on the Mac the server starts cleanly out of the box, while on Strix Halo you choose a backend (Vulkan or ROCm) first. Once running, your application layer does not care which chip is underneath.
Bottom line
In 2026, the answer to “I want lots of unified memory on a laptop” finally has two answers: Apple at the premium tier, AMD at the budget-conscious tier. For 128 GB specifically, Strix Halo at $2,800 is dramatically cheaper than MacBook Pro M4 Max 128 GB at $4,999 — and that’s the real story of this matchup.
If you don’t need 128 GB, M4 Pro wins. If you do need 128 GB and you don’t need Apple, Strix Halo is the buy. The era of one-chip-wins is finally over.