Reatan HTPC (Ryzen AI 9 HX 470 48GB)

The specs that matter for inference workloads are straightforward. The system uses unified memory architecture: the 48GB of DDR5-5600 is shared between CPU and GPU. The GPU can allocate up to 16GB of that as VRAM. This is important because it determines the maximum model size you can load without swapping to system memory.

Memory bandwidth at 90 GB/s is the bottleneck for token generation on integrated GPUs. For comparison, a dedicated RTX 4060 laptop GPU offers ~256 GB/s, but costs more and draws 115W. The Reatan HTPC’s 54W TDP makes it extremely efficient for always-on inference servers or edge deployments where thermal and power constraints are tight.

The 55 TOPS NPU is a dedicated accelerator for lightweight AI tasks like speech-to-text, image classification, or small LLM inference (e.g., Phi-3, Gemma 2B). For larger models, you’ll rely on the Radeon 890M via ROCm or DirectML. Note that AMD’s ROCm support for integrated GPUs is still maturing; Windows users will primarily use DirectML or ONNX Runtime with the NPU fallback.

This is the practical question. With 16GB of allocatable VRAM, the Reatan HTPC can comfortably run 7B-13B parameter models at 4-bit or 5-bit quantization. At Q4, a 13B model uses roughly 7-8 GB, leaving headroom for context and system processes. At Q5, the same model uses ~9-10 GB. The sweet spot for quality-to-speed is Q4_K_M on a 13B model like Llama 3.1-8B or Qwen 2.5-7B.

Expected tokens/second (using llama.cpp with Vulkan or DirectML backend):

• Llama 3.1 8B Q4_K_M: ~15-20 tokens/s
• Mistral 7B Q4_K_M: ~20-25 tokens/s
• Qwen 2.5 7B Q4_K_M: ~18-22 tokens/s
• DeepSeek-R1-Distill-Qwen-7B Q4_K_M: ~16-20 tokens/s
• Llama 3.1 13B Q4_K_M: ~10-14 tokens/s
• Mixtral 8x7B (Q3_K_M): Not feasible — 16GB VRAM is insufficient for the full MoE model even at Q3. Stick to 7B-13B range.

Larger models with patience:

• 32B models (e.g., Qwen 2.5-32B, CodeLlama-34B): Can run at Q3 (3-bit) with heavy context pruning. Expect 3-5 tokens/s and high latency. Only practical for batch inference or non-interactive tasks.
• Multimodal models (LLaVA, Qwen-VL): 7B variants fit easily. 13B variants at Q4 may leave limited room for image embeddings; use with short context.

Long-context tasks: The 48GB system memory allows large context windows (e.g., 128K tokens) at 4-bit, but the GPU VRAM is the bottleneck. For long-context, you’ll need to offload layers to CPU, which tank throughput. This machine is better for interactive chat (2K-8K context) than document-level analysis.

NPU-accelerated models: The XDNA 2 NPU can handle small on-device models (e.g., Whisper base, Phi-3-mini) at low latency. It’s useful for always-on voice assistants or real-time transcription without GPU overhead.

This system is not for training. It’s for inference, and specifically for deployment scenarios where you need a small, low-power, always-on box that can run local LLMs, agents, or RAG pipelines.

Who should buy it:

• Developers building AI-powered applications that need local inference for privacy or offline use. The 48GB RAM allows running the application stack alongside a 7B-13B model.
• Teams running edge inference servers in retail, healthcare, or manufacturing. The HTPC form factor fits in a server rack or behind a display. 54W TDP means it can run 24/7 without active cooling concerns.
• Hobbyists running local chatbots (e.g., Ollama, LM Studio) who want a dedicated machine that’s quiet and compact. The 2TB SSD provides ample space for model storage.
• Business and education fleets that need a standardized, IT-manageable device capable of running AI assistants for customer support or tutoring. The lack of OCuLink simplifies deployment and reduces failure points.

Not for:

• Heavy batch inference or high-throughput API servers (need dedicated GPU with >200 GB/s bandwidth).
• Training even small models (use a cloud GPU or desktop with RTX 4090).
• Running 70B+ models (VRAM too limited).

vs. Minisforum AI X1 Pro (Ryzen AI 9 HX 470, 32GB)

The Minisforum AI X1 Pro is a direct competitor but comes with only 32GB of single-channel RAM in many configurations. The Reatan HTPC’s 48GB dual-channel setup gives it a significant memory bandwidth advantage (90 GB/s vs. ~45 GB/s if single-channel). For AI inference, dual-channel is critical — single-channel cuts token throughput by 30-50%. The Reatan also has a larger SSD (2TB vs. 1TB often). However, the Minisforum offers OCuLink for eGPU expansion, which the Reatan omits. If you need external GPU support, the Minisforum is the better pick. If you want a self-contained, out-of-the-box inference machine with more VRAM headroom, the Reatan wins.

vs. Apple Mac Mini M4 Pro (24GB unified memory)

The Mac Mini M4 Pro offers higher memory bandwidth (~200 GB/s) and better GPU compute for ML (Metal Performance Shaders). It can run 13B models at Q4 faster (~30-40 tokens/s). But it starts at $1,399 and maxes out at 48GB (which costs $1,899). The Reatan HTPC at $899 is half the price for comparable VRAM capacity. The tradeoff is slower inference and less mature software ecosystem (ROCm vs. Metal). If you’re already in the Apple ecosystem and need maximum performance per watt, the Mac Mini is superior. If you need Windows compatibility, lower cost, and fleet manageability, the Reatan is the pragmatic choice.

When to pick the Reatan HTPC:

• Budget-constrained but need 16GB VRAM for 13B models.
• Need a Windows-based edge device with IT-friendly form factor.
• Prioritize low power and small size over raw inference speed.
• Want a system that can also serve as an HTPC or general office PC without compromise.