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Tell Us How You Use the X2 – Help Us Improve!
Compared to standalone GPU solutions, running the 235B-parameter Qwen3 MoE model locally requires at least three GeForce RTX 5090 cards, assuming no hardware modifications are made. With modification, the minimum requirement becomes two RTX 4090s with 48GB of VRAM, or four RTX 2080 Ti cards with 11 GB of VRAM each.
In terms of both technical input and financial investment, GMKtec EVO-X2 demonstrates outstanding cost-performance advantages in local AI large model deployment.
Many of our users have their own perspectives on how they would like to use EVO X2 for local AI applications. We’d love to hear from you — what would you generally like to use the GMKtec EVO-X2 for? What do you value most about it?
For example:
- Deploying large models locally to break free from cloud restrictions, achieving low-latency, highly private, and fully controllable AI experiences. For SMEs and organizations, a desktop AI supercomputer with third-party software can be used locally for product R&D, sales insights, financial assistance, process optimization, legal consulting, systematic problem search, smart customer service, and many more application scenarios.
- For AI-related students, startups, and independent developers, EVO-X2 provides a reliable local AI experimental platform, enabling model adaptation, validation, and application development.
- For content creators, its performance can handle professional tasks such as video editing and 3D modeling. Whether serving as a flexible mobile workstation or acting as a home entertainment AI hub, it’s highly versatile.
- For hardcore gamers, it not only supports AAA gaming but also integrates AI-assisted functions, delivering both immersive gaming experiences and innovative AI-powered gameplay.
Vote here:
https://docs.google.com/forms/d/e/1FAIpQLScDS8hjfnegV-ASlJeRoz8PEzg1CU8dWWJeFYiLeuLKZCwMRA/viewform
