# AMD Ryzen AI Max+ 395 Strix Halo (128GB) vs NVIDIA DGX Spark (128GB)

> **Direct Hardware Verdict**: AMD Ryzen AI Max+ 395 ('Strix Halo') and NVIDIA DGX Spark share nearly identical unified memory physics: both wire 128GB of low-power DRAM across a 256-bit bus (256 GB/s on Strix Halo LPDDR5X-8000/8533 vs 273 GB/s on DGX Spark GB10 Grace Blackwell). Consequently, GGUF token decoding speeds on 70B and 120B MoE models are within 7% of each other! Strix Halo mini-PCs (like the Framework Desktop and HP Z2 Mini G1a at $2,200–$3,300) save $1,500+ while running native x86 Windows/Linux, whereas DGX Spark ($3,999–$6,950) justifies its premium with native ARM64 CUDA, Blackwell FP4 Tensor Cores, and 200GbE ConnectX-7 clustering.

## Contender Specifications
| Metric | AMD Ryzen AI Max+ 395 Strix Halo (128GB) | NVIDIA DGX Spark (128GB Unified Memory) |
|---|---|---|
| VRAM Capacity | 128 GB LPDDR5X (~96GB–110GB GTT GPU Pool) | 128 GB LPDDR5x (~110GB Allocatable) |
| Memory Bandwidth | 256-bit (256 GB/s) | 256-bit (273 GB/s) |
| Typical Price | $2,200 – $3,300 (Mini-PC / Workstation) | $3,999 – $6,950 (New) |
| Estimated Throughput | ~4.8 tok/s (70B Q4) / ~35 tok/s (120B MoE) | ~5.2 tok/s (70B Q4) / ~38 tok/s (120B MoE) |

## Comparative Model Compatibility Matrix
| Model & Quantization | VRAM Demand | AMD Ryzen AI Max+ 395 Strix Halo | NVIDIA DGX Spark |
|---|---|---|---|
| OpenAI gpt-oss 120B MoE (Q4_K_M) | 71.2 GB | Runs Comfortably (~35 tok/s) | Runs Comfortably (~38 tok/s) |
| GLM-4.5-Air 106B MoE (Q4_K_M) | 64.8 GB | Runs Comfortably (~21 tok/s) | Runs Comfortably (~24 tok/s) |
| Meta LLaMA 3.3 70B (Q4_K_M) | 44.1 GB | Runs Comfortably (~4.8 tok/s) | Runs Comfortably (~5.2 tok/s) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.7 GB | Runs Comfortably (~52 tok/s) | Runs Comfortably (~58 tok/s) |


## Silicon Architecture & Thermal Analysis
Strix Halo combines two 8-core Zen 5 CCDs (16 x86 cores / 32 threads) with a massive 40-compute-unit RDNA 3.5 integrated GPU (`Radeon 8060S`) and a 50-TOPS XDNA 2 NPU on an advanced fan-out package. Because both Strix Halo (256 GB/s) and DGX Spark (273 GB/s) are bound by 256-bit LPDDR5X interfaces, neither box is meant for fast dense 70B token streaming (~5 tok/s); instead, both shine as silent, low-wattage (<140W) hosts for sparse Mixture-of-Experts models like `OpenAI gpt-oss-120b` (5.1B active, ~35–38 tok/s), `GLM-4.5-Air 106B` (12B active), and `Qwen3-Coder 30B-A3B` (3.3B active). Where DGX Spark pulls ahead is compute-heavy prompt prefill and native NVFP4 execution: its Blackwell Tensor Cores ingest 32k RAG prompts ~2.8x faster than RDNA 3.5 WMMA.

## Tier 1 Free Software Optimization ($0)
On Linux Strix Halo mini-PCs, set `amdgpu.gttsize=112640` and `ttm.pages_limit=28835840` in `/etc/default/grub` so ROCm and Vulkan can dynamically allocate up to 110GB of unified LPDDR5X without BIOS UMA carve-out rebooting.

## Verified Hardware & Cloud Pathways
- Buy AMD Ryzen AI Max+ 395 Strix Halo on Amazon: https://www.amazon.com/s?k=Ryzen+AI+Max%2B+395+128GB+mini+PC&tag=sparecables-20 (rel="noopener sponsored")
- Buy NVIDIA DGX Spark on Amazon: https://www.amazon.com/s?k=NVIDIA+DGX+Spark+128GB+mini+AI+supercomputer&tag=sparecables-20 (rel="noopener sponsored")
- Rent on RunPod: https://runpod.io?ref=ynypzu94 (rel="noopener sponsored")
- Rent on Vast.ai: https://cloud.vast.ai/?ref_id=723130 (rel="noopener sponsored")

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