GMKtec EVO-X2

The GMKtec EVO-X2 represents a distinct shift in compact computing for machine learning practitioners, packaging workstation-class unified memory into a mini PC chassis. Powered by the AMD Ryzen AI Max+ 395 processor (part of AMD's Strix Halo family), this system addresses the single biggest bottleneck in local AI deployment: addressable VRAM. While standard desktop hardware forces developers into multi-GPU setups or costly enterprise accelerators to load high-parameter models, the EVO-X2 pairs 16 Zen 5 CPU cores and a Radeon 8060S GPU with 128GB of high-speed unified LPDDR5X memory.

Positioned in the prosumer and developer desktop tier, the device bridges the divide between standard consumer mini PCs and dedicated AI workstations. For developers evaluating Unknown hardware for AI development and looking for the best hardware for running AI models locally, the EVO-X2 offers an efficient alternative to power-hungry multi-card rigs. With the capacity to allocate up to 96GB of system memory strictly to graphics and compute tasks, it enables local deployment of architectures that previously required distributed nodes or cloud endpoints.

Operating at a peak power envelope of 140W, the system delivers high parameter density per watt. It provides researchers, autonomous agent architects, and software engineers with a quiet, dedicated inference engine capable of running advanced open-weight models locally without incurring recurring API fees or exposing proprietary data to third-party endpoints.

The GMKtec EVO-X2 for AI builds on AMD's integrated Strix Halo APU architecture. Compute is distributed across three specialized engines: the 16-core, 32-thread Zen 5 CPU boosting up to 5.1GHz with 80MB of onboard cache, the integrated Radeon 8060S GPU with 40 RDNA 3.5 compute units, and the XDNA 2 Neural Processing Unit (NPU) rated at 50 TOPS. Across CPU, GPU, and NPU, the platform delivers a combined INT8 compute throughput of 126 TOPS.

The standout specification for deep learning workloads is the memory subsystem. The EVO-X2 features 128GB of LPDDR5X-8000 memory operating over an eight-channel bus. Because the memory is unified, up to 96GB can be dedicated directly to the Radeon 8060S compute engine. In practice, this gives local inference pipelines what effectively behaves as a 96GB GPU for AI within a mini PC form factor.

While discrete high-end GPUs like the RTX 4090 offer higher peak memory bandwidth, they are hard-capped at 24GB of VRAM per card. The EVO-X2 sidesteps PCIe bus communication overhead entirely by housing massive model weights in a single, contiguous 96GB pool. The memory bandwidth of the eight-channel LPDDR5X-8000 configuration provides the sustained throughput needed to feed the 40 compute units during autoregressive generation.

Key technical specifications include:

• Processor: AMD Ryzen AI Max+ 395 (16 Zen 5 cores, 32 threads, up to 5.1GHz boost, 80MB total cache)
• GPU Engine: AMD Radeon 8060S (40 RDNA 3.5 compute units)
• NPU: AMD XDNA 2 architecture, rated at 50 TOPS
• System Compute: 126 TOPS combined INT8 performance
• Unified Memory: 128GB LPDDR5X-8000 (eight-channel interface, up to 96GB allocatable as VRAM)
• Storage: 2TB PCIe 4.0 NVMe SSD (dual M.2 2280 expansion slots)
• Power & Cooling: 140W max TDP, Arctic dual-turbine fan cooling system with integrated vapor chamber
• Connectivity: Wi-Fi 7, Bluetooth 5.4, 2.5GbE LAN, 5x USB Type-A ports, 4x video outputs

Thermal management is handled by an Arctic vapor chamber paired with dual turbine fans. Under sustained heavy inference loads, the system manages the 140W TDP effectively, preventing thermal throttling during long prompt evaluation and token generation sessions.

GMKtec EVO-X2 VRAM for large language models makes it a specialized tool for loading high-parameter weights without CPU offloading penalties. When evaluating GMKtec EVO-X2 AI inference performance, the 96GB allocation threshold determines model viability across distinct parameter tiers:

Models such as Llama 3.1 8B, Mistral 7B, and Qwen 2.5 14B fit completely into memory at unquantized FP16 or high-fidelity Q8_0 quantization. In this tier, GMKtec EVO-X2 tokens per second remain exceptionally high, delivering rapid interactive streaming suitable for low-latency coding assistants, document parsing, and real-time voice agents.

This range represents the optimal operational tier for the machine. A model like Llama 3.3 70B or Qwen 2.5 32B runs cleanly inside the 96GB limit:

• Llama 3.3 70B (Q4_K_M): Requires roughly 43GB of VRAM. It fits entirely into the graphics pool with more than 50GB left for KV cache expansion, enabling full 64k to 128k context windows without out-of-memory errors. Expect sustained generation speeds around 10 to 14 tokens per second.
• Qwen 2.5 72B (Q4_K_S / Q4_K_M): Operates comfortably with context lengths adequate for multi-turn reasoning and retrieval-augmented generation (RAG).

The EVO-X2 is practical hardware for running 235B parameter models locally. GMKtec demonstrated local inference on a 235B parameter model at Computex 2025. Running models of this scale requires aggressive quantization, such as 2-bit (IQ2_XXS or Q2_K) or low-bit Mixture of Experts (MoE) execution. While generation speeds at 235B are lower (typically 2 to 5 tokens per second), the fact that it loads and executes fully on a 140W mini PC without multi-node clusters makes it a unique platform for large-weight testing.

The massive VRAM pool allows the EVO-X2 to excel at vision-language tasks (such as Qwen2-VL and LLaVA 1.6) and massive context tasks. A 32B model can run with a 128k context window without offloading the KV cache to system RAM, avoiding the steep speed drop associated with CPU context swapping.

The EVO-X2 serves specific operational requirements across engineering and development workflows:

• Engineers Building Local AI Agents: As teams seek the best hardware for local AI agents 2026, the EVO-X2 provides enough headroom to run a quantized 70B reasoning model alongside local vector databases, embeddings, and runtime environments simultaneously.
• Developers Running Local LLM Workflows: Practitioners running llama.cpp, Ollama, or ROCm-accelerated stacks benefit from a self-contained, low-noise environment that handles both coding models and long-context documentation queries.
• Privacy-Restricted Environments: Legal, medical, and enterprise software teams needing on-premise execution of 70B models can deploy the EVO-X2 at edge locations without building dedicated server racks.
• Inference vs. Training: This device is engineered primarily for local inference, prompt engineering, and synthetic data generation. While lightweight LoRA parameter-efficient fine-tuning (PEFT) on 8B or 14B models is fully viable within 96GB of memory, full-parameter pre-training remains the domain of enterprise clusters.

Understanding where the EVO-X2 fits requires comparing it against the two standard local AI alternatives: unified Mac hardware and discrete desktop rigs.

Apple silicon provides higher raw memory bandwidth through its 384-bit and 512-bit unified buses, giving the Mac Studio higher tokens-per-second throughput on dense 70B models. However, the EVO-X2 runs on native x86_64 architecture with open ROCm and Linux support, avoiding the architectural translation layers or proprietary Metal optimizations often needed for bleeding-edge frameworks. It also provides open storage upgradeability via dual PCIe 4.0 M.2 slots at a substantially lower price per gigabyte of VRAM.

A custom workstation equipped with two RTX 4090 GPUs provides 48GB of aggregate VRAM across dual cards and offers unmatched raw compute and CUDA software support. However, dual-4090 rigs draw 850W to 1000W under load, generate substantial heat, occupy full mid-tower cases, and cost significantly more. The EVO-X2 offers double the addressable VRAM (96GB vs. 48GB) within a 140W mini PC footprint, making it the better choice for loading models larger than 70B where memory capacity takes precedence over raw compute throughput.

For practitioners looking for the best AI chip for local deployment where VRAM capacity, compact footprint, and power efficiency are the primary design constraints, the GMKtec EVO-X2 delivers a targeted, cost-effective platform for running advanced local LLM architectures.