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Pushing Desktop AI Further: GMKtec EVO-X5 Pro Accelerates 320B GLM-5.3-Flash Inference

A 10× improvement in prefill speed and a 1.85× boost in decoding performance bring large-scale local AI inference to a new level. As AI models continue to scale into the hundreds of billions of parameters, running them efficiently on a desktop system remains a significant challenge. Large models demand substantial memory capacity, high bandwidth, and carefully optimized inference software to deliver a responsive user experience. GMKtec is pushing those boundaries with the EVO-X5 Pro Desktop AI Supercomputer. Through extensive software-level optimization by our engineering team, the EVO-X5 Pro has achieved significant performance improvements when running the 320B-class GLM-5.3-Flash model: ● Approximately 10× faster prefill for processing input prompts. ● Approximately 1.85× faster decoding for generating responses. These results demonstrate how close hardware and software integration can unlock more of the potential of large-scale AI inference on a desktop system. Built for Large Models: 192GB of Unified Memory Running a model at this scale requires more than raw processing power. Memory capacity is a critical factor in enabling large models to run locally, particularly when working with long prompts and extensive context. The GMKtec EVO-X5 Pro is powered by the AMD Ryzen AI Max+ PRO 495 processor, featuring integrated AMD Radeon 8065S graphics, and supports up to 192GB of unified memory. This large, shared memory pool gives demanding AI workloads more room to operate, helping accommodate large model weights, context, and inference workloads within a single desktop platform. By combining high-capacity unified memory with powerful CPU and integrated GPU resources, the EVO-X5 Pro provides a foundation for experimenting with and deploying large language models locally—without relying exclusively on cloud-based AI services. Software Optimization That Makes a Difference Hardware provides the foundation, but inference performance also depends on how efficiently the software uses available resources. To improve GLM-5.3-Flash performance, GMKtec's engineering team carried out targeted, low-level optimizations across the inference stack. The results show substantial gains in two key stages of model inference. Prefill: Approximately 10× Faster Input Processing Prefill is the stage in which a model processes the input prompt before beginning to generate a response. For long documents, large codebases, and context-heavy workflows, this stage can have a significant impact on how long users wait before generation begins. With the optimized configuration, the EVO-X5 Pro achieved an approximately 10× improvement in prefill performance compared with the baseline configuration used in GMKtec's testing. This can substantially reduce input-processing time and help make interactions involving large amounts of context more responsive. Decode: Approximately 1.85× Faster Response Generation Once prefill is complete, the model enters the decoding stage, generating its response token by token. GMKtec's optimizations delivered an approximately 1.85× improvement in decoding performance in the tested configuration. Faster decoding can make a practical difference in extended conversations, long-form writing, code generation, and other workloads that require the model to produce substantial amounts of output. Together, these improvements address two important parts of the inference experience: processing the information you provide and generating the response you need. Stress-Tested with Nearly 500K Tokens of Context Large models must do more than perform well with short prompts. They also need to handle demanding context lengths while maintaining consistent generation performance. To evaluate the optimized GLM-5.3-Flash configuration, GMKtec tested the EVO-X5 Pro across a range of input lengths, from 1,024 tokens to 499,712 tokens—nearly 500K tokens. The results highlight the system's ability to sustain high throughput across a broad range of context sizes. Input length Prefill speed Decode speed Time to first token 1,024 tokens 494.54 tokens/s — 2.22 seconds 499,712 tokens 628.91 tokens/s 28.23 tokens/s 795.77 seconds Source: GMKtec internal testing. Results reflect the tested hardware, software, model configuration, and inference settings. Actual performance may vary. At the shortest tested input length, the system processed the prompt at 494.54 tokens per second, with a time to first token of just 2.22 seconds. At the other end of the test, the EVO-X5 Pro processed an input of 499,712 tokens while sustaining a reported prefill throughput of 628.91 tokens per second and a decoding speed of 28.23 tokens per second. Time to first token was 795.77 seconds. That longer wait reflects the substantial amount of input the model must process before it can begin generating a response. For workloads involving exceptionally large context windows, the ability to process that volume of information locally is an important part of the performance picture. Across the tested context lengths—including 4K, 32K, and 128K tokens—decoding speeds remained in the 28–33 tokens-per-second range, according to GMKtec's internal results. These figures illustrate the importance of evaluating AI systems across different prompt sizes, rather than relying on short-prompt benchmarks alone. A Desktop AI Platform for Private, Local Workflows The EVO-X5 Pro is designed to be more than a conventional mini PC. It serves as a compact desktop AI computing platform for users who want to explore large models, build local AI workflows, and keep more of their processing on their own hardware. Potential applications include: ● Research and document analysis: Work with extensive source material and long-context prompts. ● Software development: Explore AI-assisted coding and code analysis using locally hosted models. ● AI experimentation: Test large language models and develop custom inference workflows. ● Privacy-conscious computing: Keep supported AI workloads and their data on local hardware, reducing dependence on third-party cloud inference services. Local execution can offer greater control over data and deployment, although privacy ultimately depends on the software, network configuration, and services used. For developers, researchers, and AI enthusiasts who want to push beyond conventional desktop AI workloads, the EVO-X5 Pro combines substantial memory capacity with targeted inference optimization in a compact form factor. Bringing Large-Scale AI Closer to the Desktop The progress demonstrated with GLM-5.3-Flash highlights what can be achieved when capable hardware is paired with software engineered to make better use of it. With support for up to 192GB of unified memory, the AMD Ryzen AI Max+ PRO 495 processor, and significant gains in both prefill and decoding performance, the GMKtec EVO-X5 Pro offers a powerful platform for exploring large-scale local AI inference. The next generation of AI computing doesn't have to live exclusively in the cloud. With the EVO-X5 Pro, GMKtec is bringing more of that capability to the desktop. Discover the GMKtec EVO-X5 Pro and explore the possibilities of local AI computing.