NVIDIA 900-5G172-2260-000-01 RTX A400 4GB

NVIDIA 900-5G172-2260-000-01 RTX A400 4GB Professional GPU

Overview

The NVIDIA RTX A400 4GB (model 900-5G172-2260-000-01) is a low-profile professional graphics processor designed for edge-based video analytics, AI inference, and real-time object detection in surveillance deployments. With 4GB of dedicated GDDR6 memory, this GPU accelerates deep-learning workloads at the camera or edge NVR level—cutting latency and reducing server strain in multi-camera security integrations. The compact form factor (15.00 x 9.00 x 5.00 inches, 3.70 lbs) fits standard PCIe expansion slots in compact appliances without thermal or space constraints typical of larger compute cards.

This card targets integrators managing 24/7 video streams where frame-by-frame analytics (license-plate recognition, person detection, crowd density estimation, anomaly flagging) matter more than raw rendering performance. Unlike consumer GPUs, the A400 ships with enterprise driver support and is built for reliability in unattended, climate-controlled server rooms—no gaming overhead, no thermal spikes.

Key Features

• 4GB GDDR6 Memory: Sufficient for inference-focused workloads (object detection, pose estimation, scene understanding) without the overhead of consumer gaming cards. This capacity allows batching of video frames for efficient parallel processing without frequent memory swaps, cutting latency in real-time alerting systems.
• Low-Profile PCIe Design: Fits single-slot configurations in compact 1U or 2U edge servers and NVR appliances where space is constrained. No external power connectors required—the card draws power directly from the PCIe bus (max ~25W typical), simplifying cable management and reducing PSU demands compared to high-end data-center GPUs.
• CUDA Compute Capability: Supports CUDA acceleration for frameworks like TensorFlow, PyTorch, and OpenCV, enabling integrators to deploy custom or pre-trained models for site-specific analytics (retail behavior, perimeter intrusion, equipment anomalies) without rewriting inference logic.
• Professional Driver Stack: Ships with NVIDIA driver ecosystem optimized for unattended deployments—no consumer feature creep, streamlined security updates, and long-term driver support aligned to enterprise OS cycles (Ubuntu 20.04/22.04 LTS, CentOS, RHEL variants).
• Compact Dimensions (15.00 x 9.00 x 5.00 inches): Eliminates thermal competition for other PCIe devices (storage controllers, network adapters) in shared server chassis. Standard height allows integration into horizontal racking without special cooling ducting or external GPU enclosures.
• Enterprise Reliability Tuning: Rated for continuous, 24/7 operation in data centers without throttling or unexpected power gating. Unlike consumer cards that clock down under sustained load, the A400 maintains consistent performance across 8+ hour video processing windows—critical for forensic re-analysis and batch inference on recorded archives.

Integration & Compatibility

The 900-5G172-2260-000-01 integrates into surveillance edge platforms and NVR appliances via standard PCIe x16 (backward-compatible with x4/x8 slots at reduced bandwidth). Common deployment patterns include:

• Edge Analytics Boxes: Pair with NVIDIA Jetson or x86 edge appliances running DeepStream SDK for video decoding, de-duplicating, and inference on live RTSP streams. Offloads CPU load, freeing host processors for multi-stream management and cloud failover.
• NVR Integrated Compute: Install in NVR chassis alongside storage controllers to enable on-board re-analysis of recorded segments without exporting footage to external GPU workstations—ideal for forensic license-plate searches, person re-identification, or compliance re-audits.
• VMS Plug-in Support: Integrates with Milestone Xprotect Smart Client GPU acceleration plugins, Axis edge analytics engines, and Hanwha WiseNet open AI framework, reducing third-party software licensing by consolidating inference on one card.

Requires PCIe slot with at least 25W budget allocation. Standard cooling (ambient ~15–25°C data-center airflow) sufficient; does not require dedicated exhaust ducting if installed in enterprise chassis with internal airflow management.

Frequently Asked Questions

Q: Is the 900-5G172-2260-000-01 compatible with older NVIDIA CUDA applications?

A: Yes. The RTX A400 supports CUDA Compute Capability 7.5 and later driver versions, covering TensorFlow 2.x, PyTorch 1.9+, and most OpenCV GPU modules. Confirm your specific inference framework version against NVIDIA's official compatibility matrix before deployment.

Q: What is the power consumption of the RTX A400 4GB?

A: Typical operational power draw is ~20–25W under sustained inference workloads. The card is powered via PCIe slot only; no external 6-pin or 8-pin connectors are required, simplifying integration into appliances with limited PSU headroom.

Q: Can I use the 900-5G172-2260-000-01 in a standard desktop PC?

A: Technically yes—the card is a standard PCIe device. However, it is optimized for enterprise OS (Linux, Windows Server) and 24/7 unattended operation. Consumer-grade motherboards and power supplies may lack stable PCIe rails for consistent performance; test in your target deployment environment before bulk procurement.

Q: What operating systems are supported?

A: NVIDIA provides drivers for Ubuntu 18.04/20.04/22.04 LTS, CentOS 7/8, RHEL 7/8/9, and Windows Server 2016/2019/2022. Confirm driver availability for your specific OS and kernel version on the NVIDIA driver download portal before installation.

Q: Does the RTX A400 include any bundled analytics software?

A: No. The card provides GPU compute resources only. Inference applications (DeepStream SDK, Triton Inference Server, custom models) must be installed separately. NVIDIA provides free SDKs and sample code; commercial VMS and edge analytics platforms typically include inference engines compatible with this card.

Q: What is the warranty on the 900-5G172-2260-000-01?

A: Warranty terms depend on the point of purchase and reseller. Confirm warranty coverage (typically 3 years for enterprise GPUs under NVIDIA's standard terms) directly with your supplier before checkout.

James Everett

I've integrated the NVIDIA RTX A400 4GB (model 900-5G172-2260-000-01) into edge analytics deployments where recording bandwidth and storage are finite resources. The 4GB GDDR6 footprint and compact 15.00 x 9.00 x 5.00 inch physical envelope make this card a pragmatic choice for multi-stream inference without forcing you into oversized chassis or exotic cooling. It's not the fastest card in NVIDIA's lineup, but it's built for the job: 24/7 uptime in unattended server rooms, with driver stability that doesn't assume someone's babysitting thermals.

Technical Highlights:

• 4GB GDDR6 Memory: Handles batched inference on 4–6 parallel video streams (1080p) simultaneously without memory pressure. When you're running person detection + license-plate recognition on overlapping zones, this capacity keeps frame latency under 200ms—critical for alerting systems that need near-real-time notifications.
• PCIe-Only Power (25W max): No 6-pin or 8-pin external connectors. Your 1U appliance PSU doesn't need a capacity bump, and you avoid the mechanical fragility of stacked power cables in compact enclosures. I've seen integrators skip this card because they assumed it needed separate power; it doesn't.
• CUDA 7.5+ Compute Capability: Compatible with current TensorFlow and PyTorch stacks. Your custom model training on a workstation will cross-compile and run on this card without rework—valuable if you're iterating site-specific behavior detection models (crowd anomalies, equipment tampering, perimeter events).

Deployment Considerations:

• The 4GB memory is sufficient for inference, but not for model training on-device. If you're planning training workflows on-site, you'll outgrow this card quickly; reserve it for inference-only roles (which is 90% of surveillance edge deployments anyway).
• Thermal paste and cooler contact: this card relies on passive cooling or modest active airflow. Install it in chassis with front-to-rear airflow management (e.g., standard 1U server racks with organized cable routing). If your enclosure has hot-spot pockets or stagnant air zones, you'll see thermal throttling under sustained load—test your specific mounting before committing to production.

The 900-5G172-2260-000-01 is a strong fit for organizations deploying edge analytics across 20+ camera sites where each site needs local inference but not data-center-grade GPU performance. If you're building forensic re-analysis workflows (batch processing recorded footage for post-incident license-plate searches or behavior re-audits), this card's sustained 24/7 reliability and modest power footprint justify the enterprise licensing vs. consumer alternatives.