GMKtec EVO-X2 (Ryzen AI Max+ 395 128GB)

The GMKtec EVO-X2 (Ryzen AI Max+ 395 128GB) is a flagship mini PC purpose-built for local AI inference. It leverages AMD’s Strix Halo APU — a 16-core Zen 5 CPU paired with a Radeon 8060S iGPU and XDNA 2 NPU — to deliver 96 GB of unified GPU-allocatable memory in a compact, low-power chassis. At $1,999, it occupies the prosumer tier: more capable than a consumer laptop, far more energy-efficient than a multi-GPU workstation, and directly competitive with entry-level server-grade inference rigs.

This is not a general-purpose desktop. The EVO-X2 is engineered for one task: running large language models and other AI workloads locally, without cloud dependency. Its 128 GB of LPDDR5X-8000 unified memory can allocate up to 96 GB to the GPU, enabling models that would otherwise require a $10,000+ setup. The design won the American Good Design Platinum Award 2025, reflecting its industrial and thermal engineering — a metal chassis with triple-fan cooling keeps sustained loads under 70 °C.

For developers and researchers who need to run 70B-parameter models at Q5 quantization, or experiment with sparse 235B models, the EVO-X2 is currently the most accessible hardware that can do so in a desk-friendly form factor.

The headline spec is the 96 GB of GPU-accessible memory. This is not a discrete GPU with dedicated VRAM; it’s unified memory shared between the CPU and GPU via the Radeon 8060S iGPU (40 RDNA 3.5 compute units). The full 96 GB allocation is unlocked by default on this SKU — no BIOS tweaks or memory reservation tricks required. This is the largest unified memory pool available in any mini PC as of mid-2025.

The 128 GB of LPDDR5X-8000 runs on a 256-bit bus, delivering 256 GB/s bandwidth. That’s roughly 60% of a desktop RTX 4090 (1 TB/s) but more than enough for token generation at reasonable batch sizes. For context, a Mac M2 Ultra with 192 GB unified memory peaks at 800 GB/s, but costs over $5,000. The EVO-X2’s bandwidth is a limiting factor for very large models (235B+), but for the 70B–120B range it’s well-matched to the compute throughput.

The XDNA 2 NPU provides 50 TOPS for integer operations, but the real workhorse for LLM inference is the iGPU’s FP16/INT4 matrix units. The Radeon 8060S iGPU delivers approximately 12.5 TFLOPS (FP16) — comparable to an RTX 4060 Ti. Combined with the CPU’s 16 Zen 5 cores (boost up to 5.1 GHz), the system achieves ~50 tokens/s on 7B models and ~23 tokens/s on 20B models in real-world benchmarks (e.g., GPT-OSS-20B at 23.8 tok/s out of the box on Ubuntu).

At 140W peak TDP, the EVO-X2 consumes less than a third of a typical gaming GPU. This makes it viable for edge deployment, home labs running 24/7, or offices where noise and heat matter. The triple-fan cooling keeps the APU under 70 °C during sustained inference, though brief throttling may occur after 15 minutes of peak load (per Mini PC Reviewer tests).

• APU: AMD Ryzen AI Max+ 395 (16C/32T Zen 5, Radeon 8060S 40 CUs, XDNA 2 NPU)
• Memory: 128 GB LPDDR5X-8000 (256-bit, 256 GB/s)
• GPU VRAM Allocation: Up to 96 GB
• Storage: 2 TB PCIe 4.0 NVMe (Crucial P3)
• Connectivity: USB4, Wi-Fi 7, BT 5.4, SD Card 4.0, dual LAN (2.5 GbE)
• Display: Quad 8K via HDMI 2.1 / DisplayPort 1.4
• OS: Windows 11 Pro (also tested with Ubuntu 24.04.3 LTS)

This is the critical question for any practitioner. The EVO-X2’s 96 GB unified memory and 256 GB/s bandwidth define a clear capability envelope.

• 70B at Q5_K_M: This is the ideal workload. Models like Llama 3.1-70B, Qwen 2.5-72B, or Mistral Large 2 fit comfortably with ~48 GB VRAM usage, leaving room for context (up to 128K tokens). Expect 10–15 tok/s on a single prompt.
• 120B at Q4: Models such as DeepSeek-R1-120B or Mixtral 8x22B (MoE) can be run with careful context management. Real-world tests show ~15 tok/s for GPT-OSS-120B.
• 235B Sparse: Qwen3-235B (sparse MoE) can be loaded at ~50% density, using nearly all 96 GB. Token generation slows to ~11 tok/s — usable for batch inference or offline analysis, but not real-time chat.

The unified memory architecture excels at multimodal models (e.g., LLaVA, Qwen-VL) because image embeddings share the same pool without PCIe transfers. Long-context tasks (128K+ tokens) are feasible for models up to 70B; beyond that, context length must be reduced to avoid OOM.

Based on benchmarks from nishtahir.com and Mini PC Reviewer:

• 7B Q4_K_M: ~50–55 tok/s (prompt eval >1900 tok/s)
• 20B Q5_K_M: ~23 tok/s
• 70B Q5_K_M: ~11–14 tok/s
• 120B Q4: ~5–8 tok/s (prompt eval slower due to bandwidth)

For production inference servers, batch size 1 is typical; for hobbyist use, these speeds are comfortable for interactive chat on models up to 70B.

• Local LLM Enthusiasts who want to run 70B models without cloud subscriptions or noisy GPU rigs. The EVO-X2 is a silent, compact alternative to a dual RTX 4090 setup.
• AI Application Developers building agentic workflows that require private, low-latency inference. The 96 GB unified memory allows serving multiple models simultaneously (e.g., a 7B router + 70B generator).
• Edge AI Deployments where power and space are constrained but model size cannot be compromised. At 140W peak, it can run off a standard outlet in a retail kiosk, medical device, or field lab.
• Research Teams needing to fine-tune or experiment with 70B–120B models on a single machine. While not designed for training (no discrete GPU), the EVO-X2 can handle LoRA fine-tuning on small batches.

• High-throughput inference servers serving hundreds of concurrent users — you need multiple GPUs with higher bandwidth.
• Training large models from scratch — the iGPU’s 12.5 TFLOPS is insufficient for pre-training.
• Gamers — the Radeon 8060S can handle 1440p at medium settings, but a discrete GPU is better for high-refresh gaming.

• Price: EVO-X2 $1,999 vs. Mac Studio ~$5,000+ for 192 GB.
• VRAM: 96 GB vs. 192 GB — Mac Studio wins for 120B+ models.
• Bandwidth: 256 GB/s vs. 800 GB/s — Mac Studio is faster for token generation.
• Software: EVO-X2 runs Windows/Linux with full CUDA/ROCm compatibility via Vulkan/LLVM; Mac Studio limited to Metal and Apple’s stack.
• Pick EVO-X2 if: you need Linux-native tooling (llama.cpp, vLLM, Ollama) and a lower budget.

• VRAM: 96 GB vs. 24 GB — EVO-X2 can run 70B models that a 4090 cannot fit.
• Speed: 4090 generates tokens ~2–3× faster for models that fit (e.g., 7B at 150 tok/s).
• Power: 140W vs. 450W — EVO-X2 is far more efficient.
• Pick EVO-X2 if: your models exceed 24 GB VRAM, or you need a silent, compact system.

• VRAM: 96 GB vs. 64 GB — EVO-X2 supports larger models.
• Bandwidth: 256 GB/s vs. 273 GB/s — comparable.
• Price: $1,999 vs. $1,399 — Mac Mini is cheaper for smaller models.
• Pick EVO-X2 if: you need to run 70B+ models or prefer x86 ecosystem.

The GMKtec EVO-X2 is currently the most cost-effective way to get 96 GB of unified memory for AI inference in a compact, low-power package. It fills a gap between consumer laptops and enterprise workstations, making large local models accessible to individual developers and small teams.