Models· Important· 🧪 Worth Testing

Scaling Large MoE Models Across Multiple NVIDIA DGX Stations

In short: NVIDIA has released a technical workflow for serving the 753B parameter GLM-5.3 MoE model across two GB300-equipped DGX Stations. By utilizing tensor parallelism (TP=2) over RDMA connections and vLLM, you can overcome single-GPU memory constraints (252 GB HBM3e) for massive frontier-scale models.

Source: NVIDIA Developer13 Nov 2026, 19:00NVIDIAGLM-5.3 NVFP4 Original article ↗

This summary was generated automatically by AI from NVIDIA Developer's publication. It is our own text, not a copy of the original — facts, figures and quotes belong to the source, linked above and below.

What changed?

1Deployment of GLM-5.3 NVFP4 via tensor parallelism across two networked stations
2Implementation of FP8 KV-cache to optimize memory footprint
3Enablement of multi-token prediction (4 tokens) for improved throughput
4Utilization of RDMA (ConnectX-8) for low-latency node communication

GLM-5.3 NVFP4

Parameter	Before	Now
Context window	Not specified	98304 tokens
Speculative decoding	Not specified	Multi-token prediction (4 tokens)
KV-cache	Not specified	FP8 storage

Why it matters

This approach enables your team to deploy massive, state-of-the-art models on private infrastructure while maintaining high throughput. It solves the physical memory bottleneck by effectively pooling GPU capacity across distinct hardware units.

What it means for AI agents and contact centers

When testing custom local agents, use this pattern if your models exceed HBM3e limits. Monitor latency overhead added by RDMA inter-node communication compared to single-node setups. Evaluate how the FP8 cache and speculative decoding influence transcription and reasoning speed for your specific workload.

🧪 Worth Testing

It is a standard framework for scaling high-parameter models on private infrastructure where cloud API latency or privacy policies are concerns.

Distributed vLLM inference on DGX Stations· New

vLLM Inference MoE Distributed Computing RDMA Quantization LLM

Sources

NVIDIA DeveloperOfficialPrimary source
„When One GPU Isn’t Enough: Serving GLM-5.3 Across Two NVIDIA DGX Stations with vLLM“
13 Nov 2026, 19:00
Original article →

Published by source: 13 Nov 2026, 19:00
Found by our system: 13 Nov 2026, 19:19
Summary generated: 13 Nov 2026, 19:20

This article was written by AI from the original source. Facts, numbers and prices come from the source; missing values are marked “Not specified”. Legal notice, copyright and privacy