Two NVIDIA DGX Stations can now work together to run a frontier large model, but the hardware threshold for bringing AI capabilities on-premises has risen sharply. NVIDIA Developer demonstrated accessing GLM-5.3 through a single service endpoint, with model weights and compute split across two DGX systems.
What this is
The deployment connects two DGX Stations over ConnectX-8 RDMA networking and uses vLLM for tensor parallelism: splitting each model computation across multiple GPUs so they can complete it collaboratively. The model also uses the NVFP4 quantization format to store weights at lower precision, reducing memory consumption.
The key change is not model capability but the delivery model: even if a single workstation cannot hold all model parameters, multiple high-end systems can jointly support one model. This has practical value for enterprises that need to retain data, control latency, and centrally manage models.
Industry view
We believe this approach will push the industry toward a model in which scale is no longer dictated by the memory capacity of a single machine, while benefiting the high-speed interconnect, inference software, and server operations ecosystems. The combination demonstrated by NVIDIA still depends on expensive, specialized hardware, and cross-node communication may introduce latency, reliability, and operational complexity. vLLM's stability under real production workloads still needs to be validated.
Another risk is a mismatch between costs and benefits: model deployment is shifting from “buying an application” to “procuring compute, configuring networking, and maintaining the system continuously,” which may push some enterprises toward cloud services. If hardware investment cannot be amortized through frequent usage, self-built infrastructure may not be more economical than paying for capacity on demand.
Impact on regular people
For enterprise IT: Large models are entering the cross-device deployment stage, requiring IT teams to evaluate compute, networking, model quantization, and ongoing operations together. Budgets will also expand from software licenses to the full infrastructure stack.
For individual workers: Enterprise-owned models may be better aligned with internal documents and workflows, making document processing, customer service, and data analysis more reliable. At the same time, rising deployment barriers mean access to model capability will not automatically translate into employee productivity.
For the consumer market: Local inference is likely to strengthen privacy-focused AI products and allow more vendors to switch between cloud and on-device services. In the short term, however, the concentration of advanced hardware may continue to push up prices for related products.