One GPU, More Work.

We evaluate Nodus Compute’s resident, software-defined GPU partitioning against native NVIDIA Multi-Process Service (MPS) on the same NVIDIA A100-SXM4-40GB. Using Qwen3-1.7B-FP8 and an identical deterministic two-tenant trace, Nodus delivered 15.0% and 15.1% more completed output throughput at 12 and 16 requests per second, while reducing p99 time to first token by 15.0% and 16.6%. The report includes the matched methodology, results, system interpretation, and reproducibility statement. Machine-readable evidence is available at https://research.nodus-compute.ai/gpu-partitioning.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-30
DOI
https://doi.org/10.5281/zenodo.23800557
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
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article

One GPU, More Work.

Pranav Varshney, Viswaretas Kotra
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Zenodo (CERN European Organization for Nuclear Research)
Parallel Computing and Optimization Techniques
article

One GPU, More Work.

Pranav Varshney, Viswaretas Kotra
article en

Abstract

We evaluate Nodus Compute’s resident, software-defined GPU partitioning against native NVIDIA Multi-Process Service (MPS) on the same NVIDIA A100-SXM4-40GB. Using Qwen3-1.7B-FP8 and an identical deterministic two-tenant trace, Nodus delivered 15.0% and 15.1% more completed output throughput at 12 and 16 requests per second, while reducing p99 time to first token by 15.0% and 16.6%. The report includes the matched methodology, results, system interpretation, and reproducibility statement. Machine-readable evidence is available at https://research.nodus-compute.ai/gpu-partitioning.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 9%
Parallel Computing and Optimization Techniques
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One GPU, More Work. — Pranav Varshney, Viswaretas Kotra · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS