# Enable Real-Time AI for High-Speed Data Acquisition with DAQIRI

[NVIDIA Developer Blog](https://yomu.fyi/company/nvidia-developer-blog) · Cara Laasch · Jun 22, 2026

**Type:** Announcement

## Summary

Scientific instruments and high-bandwidth sensors generate immense data volumes, but traditional architectures rely on a store-then-analyze model that bottlenecks real-time discovery and forces extensive data discarding. NVIDIA DAQIRI shifts data acquisition to a software-centric pipeline that connects streaming detectors directly to GPU computing. Built as part of the NVIDIA Holoscan Platform, DAQIRI uses DPDK to bypass the Linux kernel and move UDP and RoCE v2 traffic directly from the NIC into GPU DMA buffers at line rates exceeding hundreds of gigabits per second. The system automates packet reordering, flow steering, and on-GPU data conversions such as mapping wire int4 payloads into fp16 tensors using YAML configuration files and simple C++ or Python APIs. This enables real-time edge processing, filtering, and model inference across experimental pipelines like CERN ATLAS.

## Context

Modern scientific instruments and detectors, such as LCLS-II and HL-LHC, produce extreme data rates that overload traditional collect-store-analyze architectures, forcing online systems to reject significant fractions of raw data due to kernel bottlenecks and CPU overhead.

## Approach / What changed

NVIDIA DAQIRI provides a high-performance networking library using DPDK kernel-bypass to deliver zero-copy UDP and RoCE v2 packet streams directly from ConnectX NICs into GPU DMA buffers, assembling them into GPU-ready tensors via YAML configurations.

## Takeaways

- DAQIRI completely bypasses the Linux kernel using DPDK to deliver zero-copy, line-rate data transfers from NICs directly to GPU DMA buffers.
- The architecture handles automated packet reordering, flow steering, and in-flight payload conversion, such as transforming wire int4 data to fp16 tensors on the GPU.
- The A-GHOST project at CERN uses DAQIRI to run AI models on ATLAS collision streams that would otherwise be discarded by nominal selection systems.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Performance](https://yomu.fyi/topic/performance), [Streaming](https://yomu.fyi/topic/streaming)

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