GMKtec EVO-X2 owners share 128GB use cases

128GB-class machines like the EVO-X2 are turning local AI from a hobby into a private inference appliance. On this box, "VRAM" is really shared LPDDR5X, so the real story is unified memory, bandwidth, and long context rather than discrete-GPU bragging rights.

• –GMKtec's own EVO-X2 page positions the 128GB config for huge local workloads, listing support for DeepSeek-R1 70B and Qwen3 235B. That suggests the product is meant to be a local inference box, not just a fast mini PC. [Official page](https://www.gmktec.com/products/amd-ryzen%E2%84%A2-ai-max-395-evo-x2-ai-mini-pc)
• –The most practical first wins are exactly what commenters suggest: roleplay, coding assistants, and headless serving via TabbyAPI or text-generation-webui, because that's where a fat memory pool feels immediately different from a 24B Q4 rig. [Reddit thread](https://www.reddit.com/r/LocalLLaMA/comments/1s1oxe7/what_are_you_doing_with_your_60128gb_vram/)
• –Fine-tuning is possible at this tier, and one commenter calls 128GB a sweet spot for 72B dense QLoRA. The catch is still AMD's ROCm/Vulkan tooling, so inference experiments will usually pay off before training experiments.
• –Compared with GPT or Claude Sonnet, local 120B-class models usually trade some polish for privacy, cost control, and the freedom to push context windows and custom workflows as far as you want.
• –Once the serving stack works, image/video generation and private knowledge-base assistants become the natural next layer, not the main reason to buy the machine.