# Running Low-Latency Analytical Workloads with GPU-Accelerated Presto on NVIDIA GB200 NVL72

[NVIDIA Developer Blog](https://yomu.fyi/company/nvidia-developer-blog) · Tanya Lenz · Jul 8, 2026

**Type:** Benchmark

## Summary

GPU-accelerated Presto integrates NVIDIA cuDF algorithms and NVLink to deliver low-latency analytical query execution on massive datasets. Benchmarks derived from TPC-H across scale factors 1K and 3K reveal that a single NVIDIA DGX B200 system with eight GPUs outperforms 8- to 10-node Intel Xeon clusters by up to 8.2x. Scaling out across multinode NVIDIA GB200 NVL72 systems paired with IBM Storage Scale achieved additional performance gains. Enabling NVIDIA GPUDirect Storage, tuning I/O parameters, adjusting thread counts, and applying query rebatching delivered a cumulative 64% reduction in query runtimes. Topology-aware GPUDirect Storage cold reads proved roughly twice as fast as standard POSIX reads by avoiding CPU memory staging and NUMA boundary crossing penalties.

## Context

Analytical query workloads on large datasets often suffer from execution and I/O latency bottlenecks when relying on conventional multi-node CPU-based database engines and standard POSIX storage reads.

## Approach / What changed

Evaluate GPU-accelerated Presto using cuDF on single-node NVIDIA DGX B200 and multinode NVIDIA GB200 NVL72 clusters with IBM Storage Scale, applying GPUDirect Storage, 16 MiB I/O tasks, expanded I/O threads, and exchange rebatching.

## Takeaways

- A single DGX B200 running eight GPUs delivered 8.2x faster query runtimes at scale factor 1K than an eight-node Intel Xeon 6642Y cluster.
- NVIDIA GPUDirect Storage cold reads achieved roughly 2x faster runtimes than POSIX reads on GB200 NVL72 by bypassing host memory bounce buffers and avoiding NUMA boundary crossing.
- I/O optimizations, 16 I/O threads, rebatching, and rewriting query Q11 to an INSERT INTO statement yielded an overall 64% query runtime reduction across 32 GPU workers on GB200 NVL72.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Open Source](https://yomu.fyi/topic/open-source), [Performance](https://yomu.fyi/topic/performance), [Scalability](https://yomu.fyi/topic/scalability)

[Read original post](https://developer.nvidia.com/blog/running-low-latency-analytical-workloads-with-gpu-accelerated-presto-on-nvidia-gb200-nvl72)