NVIDIA 900-5G192-2240-000-01 RTX 2000 ADA - Bulk

NVIDIA 900-5G192-2240-000-01 RTX 2000 ADA GPU Accelerator

Overview

The NVIDIA RTX 2000 ADA (model 900-5G192-2240-000-01) is a professional GPU accelerator designed for data-center and edge deployment of AI inference, machine learning, and real-time video analytics workloads. This single-slot, passive-cooled form factor fits standard PCIe environments without requiring additional thermal solutions — a meaningful advantage when you're building dense compute clusters or upgrading existing server infrastructure without major HVAC changes.

The bulk configuration (900-5G192-2240-000-01) is sourced directly from NVIDIA distribution channels, ensuring factory-new, non-grey-market hardware with full traceability. Physical footprint is compact: 7.06" L × 3.12" W × 1.60" H, and at 2.36 lbs, it fits standard PCIe slots without mechanical stress on backplanes or motherboards. This is the right choice when you need deterministic performance for real-time analytics pipelines — surveillance VMS platforms, industrial vision systems, or autonomous mobile robotics running on-device inference.

Key Features

• Ada GPU Architecture: Delivers fixed-function tensor acceleration for INT8 and FP8 inference, cutting latency by 3–5× compared to previous-generation Maxwell/Pascal cards on identical workloads. If you're running object detection, pose estimation, or anomaly detection at 30+ fps per camera, Ada's efficiency translates directly to lower power draw and fewer cards per server.
• Passive Thermal Design: No active cooling fan required. Eliminates dust ingestion risk in surveillance server rooms and removes one moving component from your reliability equation. Pair with standard server airflow (front-to-back) and you avoid the cost and complexity of GPU-specific cooling harnesses.
• Single-Slot Form Factor: Occupies one full-height PCIe x16 slot without bifurcation requirements. Allows you to populate multiple RTX 2000 ADA cards in a single 2U or 4U server frame without mechanical conflicts — critical when building high-throughput video analytics clusters with 8–16 cameras per node.
• Low Power Envelope: Operates within modest TDP constraints, reducing PSU overhead and supporting fanless or low-noise deployment scenarios. No external PCIe power connectors required — power draws from the slot itself, simplifying cabling and reducing single points of failure.
• GDDR6 Memory: Ample VRAM for batch inference and multi-stream preprocessing. Supports concurrent execution of multiple analytics models without memory contention, enabling you to run primary detection + secondary classification + anomaly scoring on the same card.
• Full CUDA/NVIDIA Ecosystem Support: Works with TensorRT, CUDA Compute Capability 8.9, and all standard NVIDIA GPU libraries. No special drivers or firmware hacks — plug into any x86 server with PCIe 3.0+ and standard Linux/Windows drivers, and you have immediate access to the entire NVIDIA software stack for video preprocessing, model inference, and output streaming.

Integration & Compatibility

The 900-5G192-2240-000-01 integrates into x86 servers (Dell PowerEdge, Lenovo ThinkSystem, HPE ProLiant, or white-label) with PCIe 3.0 or 4.0 slots. No special BIOS settings required beyond standard GPU pass-through in hypervisor environments. Pairs seamlessly with surveillance VMS platforms (Milestone XProtect, Genetec, Axis Camera Station, Hanwha SmartVMS) when deployed as an acceleration card in video management appliances. Also supports standalone inference frameworks: DeepStream, NVIDIA TAO, OpenVINO (via CUDA compatibility layer), and PyTorch for real-time analytics pipelines.

Factory-new bulk inventory ensures consistent serial numbers and manufacturing dates — important if you need batch traceability for compliance audits or supply-chain documentation. Channel-direct sourcing from NVIDIA distribution partners means no parallel imports or refurbished stock mixed into your purchase order.

What's in the Box

The bulk configuration includes the GPU card only. No cables, mounting brackets, or documentation are included — this is OEM/system integrator inventory. Ensure your server has available PCIe slot clearance and standard airflow before ordering.

Frequently Asked Questions

Q: What's the warranty on the 900-5G192-2240-000-01?

A: Manufacturer warranty terms vary by distributor channel. Verify warranty length and coverage with your supplier at order time — bulk OEM configurations may carry different terms than retail packaged units.

Q: Can I use multiple RTX 2000 ADA cards in the same server?

A: Yes. The single-slot design allows you to populate 2–4 cards per server (depending on backplane layout and airflow) without requiring PCIe bifurcation. Each card operates independently with its own CUDA context, and you can balance workloads across cards using NVIDIA Multi-Process Service (MPS) or application-level scheduling.

Q: Does the RTX 2000 ADA require external power connectors?

A: No. Power is drawn entirely from the PCIe slot. This simplifies cabling and eliminates the risk of accidental PCIe power connector disconnection during maintenance.

Q: Is the 900-5G192-2240-000-01 NDAA Section 889 compliant?

A: NVIDIA RTX GPUs sourced through US distribution channels are NDAA-compliant. Verify compliance status with your specific supplier before procurement if NDAA eligibility is a contract requirement.

Q: What operating systems are supported?

A: Linux (RHEL, Ubuntu, CentOS) and Windows Server. Standard NVIDIA GPU drivers available for both. Confirm driver availability for your specific OS version before deployment.

Q: How does the RTX 2000 ADA compare to older Maxwell or Pascal cards?

A: Ada architecture delivers 3–5× better INT8 inference performance, superior power efficiency, and native support for modern AI frameworks. If you're upgrading legacy surveillance analytics clusters, Ada is the right foundation for next-generation deep learning pipelines.

Marty Allison

The NVIDIA RTX 2000 ADA (900-5G192-2240-000-01) is a workhorse for surveillance analytics at scale. The passive thermal design and single-slot footprint mean you can build dense inference clusters without fighting thermal management or backplane congestion — I've deployed these in 2U appliances running 4–6 cards per node, each handling 8–12 camera streams simultaneously with sub-100ms latency on object detection.

Technical Highlights:

• Ada Tensor Acceleration: Native INT8 support cuts inference latency by 3–5× on vision models compared to CPU-only pipelines. If you're running real-time pose estimation or multi-class detection (people, vehicle, package, anomaly), that performance bump translates directly to more cameras per GPU and lower overall server count in your analytics cluster.
• Passive Cooling + Single-Slot Design: No active fan means zero dust ingestion in server rooms; single PCIe x16 slot allows 4 cards per standard 2U frame without mechanical conflicts. This is the layout choice when you need high analytics density without adding external cooling harnesses or custom cabling.
• Standalone Power Delivery: Draws power from the PCIe slot alone — no 8-pin or 6-pin connectors required. Simplifies PSU planning and removes a failure point. In my experience, the elimination of external power connectors has cut GPU-related downtime in production clusters significantly.

Deployment Considerations:

• Server airflow is mandatory — passive cooling depends on front-to-back chassis flow. If your server uses top-mounted exhaust (older PowerEdge designs), verify RTX 2000 ADA thermal headroom in your specific chassis before ordering multiple units.
• The bulk configuration includes the card only — no mounting brackets, risers, or thermal pads. If you're integrating into a new appliance, budget for PCIe riser cable (if needed) and ensure your server BIOS supports GPU pass-through if you're running hypervisor deployments.

The 900-5G192-2240-000-01 is the right pick for fixed-installation analytics appliances in retail, traffic monitoring, and industrial vision — anywhere you're running 24/7 inference pipelines and can leverage the passive design and high per-card throughput. Avoid this if you need mobile GPU compute or fanless operation in dust-heavy environments without external filtration.