GMKtec Has Unveiled New EVO-X5 Pro AI Computer
The powerful desktop unit enables users to run massive large language models offline.
Updated on Sept. 20, 2026 in Artificial Intelligence
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GMKtec introduced its new EVO-X5 Pro desktop AI supercomputer at IFA 2026. Designed for heavy enterprise workloads, the machine allows developers to process 300-billion-parameter models without needing cloud infrastructure.
Why it matters
The system seeks to reduce dependence on cloud-based AI infrastructure, offering a secure, local solution for sensitive enterprise research. Its design targets institutional users who require reliable, constant uptime for high-intensity tasks.
The EVO-X5 Pro features the AMD Ryzen AI Max+ PRO 495 processor with 16 Zen 5 cores and an NPU delivering 55 TOPS. The system supports up to 192GB of LPDDR5X-8533 memory, with as much as 160GB available for dynamic graphics allocation.
The players
GMKtec
This hardware manufacturer headquartered in Shenzhen, China, specializes in high-performance computing devices.
AMD
The semiconductor company provides the Ryzen AI Max+ PRO 495 processor powering the new supercomputer.
The details
The device integrates a dTPM 2.0 security chip and an RJ45 DASH network adapter to ensure hardware-level protection and management. Validated for 24/7 enterprise operation, the system represents a significant shift in local compute capabilities.
Timeline
GMKtec unveiled the EVO-X5 Pro on September 19, 2026, at IFA 2026.
The device is scheduled for retail launch on September 28, 2026.
The Tech Race
This release reflects the broader industry move toward local edge computing to bypass the limitations of cloud-based AI. By deepening cooperation with the ROCm software ecosystem, GMKtec and AMD are positioning themselves to compete with established enterprise server providers.
Enterprise users and developers gain the ability to run massive AI models securely on a local workstation without ongoing cloud subscription costs. This allows for increased privacy and data control during sensitive research and development workflows.
The takeaway
The move toward high-capacity local processing power signals a pivot away from total reliance on remote data centers for AI operations. Organizations should consider how local hardware deployments could improve both security and latency for their proprietary machine learning models.
Further reading
Explore more developments in the field by visiting our Artificial Intelligence section.
Source note: This article includes information reported by The Berkshire Eagle.
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