NEW YORK, July 16, 2026 /PRNewswire/ — BOSGAME, a technology-based electronic product brand, has demonstrated distributed inference of the DeepSeek-V3.1 671B-parameter model on its AI MAX+ 395 7-Node Cluster. Built from seven BOSGAME M5 AI Mini PCs connected through USB4 Direct Connection, the system presents a scalable approach to private AI deployment and local large language model (LLM) inference.
As AI models continue to expand in parameter size and complexity, a single AI PC may lack the memory and computing resources required for advanced workloads. While traditional AI servers can provide the necessary computing power, their high deployment costs and infrastructure complexity can limit adoption. Enterprises and developers are therefore exploring scale-out architectures that support larger models while providing greater flexibility than conventional AI server deployments.
Against this backdrop, the BOSGAME AI Cluster offers a modular, cost-effective approach that grows with users’ needs.
The BOSGAME demonstration shows how seven M5 AI Mini PCs can operate as a unified AI Cluster. By distributing the inference process across multiple nodes, the cluster supports models that may exceed the memory capacity of a standalone system. Its ability to run the DeepSeek-V3.1 671B model highlights the potential of distributed computing for locally deployed generative AI applications.
Across seven nodes, the cluster provides 896GB of Unified Memory, with up to 672GB allocated as Unified VRAM, creating a substantial resource pool for memory-intensive AI workloads. USB4 Direct Connection enables the nodes to work together without requiring a traditional centralized server architecture.
Designed around a modular scale-out architecture, the BOSGAME AI Cluster allows organizations and developers to begin with one M5 AI Mini PC and expand capacity as project requirements evolve. This flexible expansion model supports workload-based growth and offers a simpler alternative to traditional AI servers while aligning infrastructure investment with actual computing needs.
Beyond AI inference, the platform also supports local AI development through a local API service powered by Llama.cpp. The cluster can be used for software development, debugging, Kubernetes operations, enterprise AI initiatives, and edge AI computing applications. By keeping workloads and data on local infrastructure, organizations can maintain greater control over data management and deployment environments.
The demonstration reflects growing demand for scalable private AI infrastructure capable of supporting increasingly large models. Through distributed inference, modular expansion, and local deployment, BOSGAME aims to provide flexible computing options for enterprises, developers, and edge AI applications.
For more information, visit: www.bosgamepc.com.
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