AMD Ryzen AI Max+ 395 Strix Halo (128GB) vs NVIDIA DGX Spark (128GB)
AMD Ryzen AI Max+ 395 Strix Halo (128GB)
NVIDIA DGX Spark (128GB Unified Memory)
Popular AI Models 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, Bandwidth & Thermal Realities
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.
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.
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Frequently Asked Questions
Why do AMD Strix Halo and NVIDIA DGX Spark have almost identical tok/s on GGUF LLMs?
Both systems use a 256-bit LPDDR5X memory bus (256 GB/s vs 273 GB/s). Since autoregressive token decoding at batch size 1 is limited by memory bandwidth, their raw GGUF Q4_K_M generation speeds are nearly identical.
Which compact 128GB box is better for a solo developer homelab?
If you primarily use llama.cpp, Ollama, or LM Studio with GGUF models and want a daily-driver x86 Linux/Windows workstation, Strix Halo saves over $1,500. Choose DGX Spark if your workflow depends on vLLM, TensorRT-LLM, or native CUDA training scripts.
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