How two GPUs serve a model that cannot fit on one

Name: How two GPUs serve a model that cannot fit on one
Uploaded: 2026-11-15T03:01:30.463Z
Duration: 219 s
Description: NVIDIA’s two-Station GLM-5.3 demonstration explains tensor parallelism through a concrete memory constraint. Follow the checkpoint into a shared serving endpoint, then learn why communication, cache capacity and workload latency require separate checks.

Explain how tensor parallelism lets two GPUs serve one oversized model and identify the memory, communication and workload checks needed to evaluate that deployment.

3:39Published November 14, 2026Revision 1

NVIDIA’s two-Station GLM-5.3 demonstration explains tensor parallelism through a concrete memory constraint. Follow the checkpoint into a shared serving endpoint, then learn why communication, cache capacity and workload latency require separate checks.

Sources

Serving GLM-5.3 Across Two NVIDIA DGX Stations with vLLM — NVIDIA Developer; presenter Fatima Fal
Serve LLMs with vLLM Across DGX Sparks — NVIDIA
GLM-5.3 Optimizations, Part 2: PCP, DCP, and Hybrid HiSparse on 16 H200s — vLLM Team

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