PNY VCNT10008GB-LLP NVIDIA T1000 Gcard LLP SCB Retail

PNY VCNT10008GB-LLP Compute GPU for Surveillance Analytics and Video Processing

Overview

The PNY VCNT10008GB-LLP is an 8GB GDDR6 compute card built around NVIDIA T1000 architecture, delivering 2.5 TFLOPs of single-precision performance with 896 CUDA cores. This is a single-slot, low-profile card drawing 50W maximum power—purpose-built for on-premise surveillance analytics servers, video transcoding pipelines, and AI-driven object detection workloads where you need GPU acceleration without dedicated cooling infrastructure or exotic power supplies. The 160 GB/s memory bandwidth and PCIe 3.0 x16 interface ensure that frame-based video data moves through the pipeline efficiently, keeping latency predictable in real-time deployments.

Key Features

• 8GB GDDR6 Memory: Sufficient for mid-scale deep learning inference (object detection, person counting, vehicle classification) or simultaneous transcoding of 4–8 video streams on a single card. The 128-bit memory interface and 160 GB/s bandwidth prevent bottlenecks when feeding frames from a video management system into analytics models.
• 896 CUDA Cores: Handles parallel compute tasks—frame preprocessing, model inference, post-processing—at a price point that makes sense for 10–50 camera deployments. Not enterprise-class, but adequate for small-to-mid commercial surveillance networks running NVIDIA DeepStream, TensorRT, or OpenCV GPU kernels.
• 2.5 TFLOPs Single-Precision Performance: Sufficient for real-time video analytics workloads; transcoding 1080p or 2K video at 30 fps per stream uses roughly 0.5–1 TFLOP per concurrent stream, leaving headroom for multiple parallel tasks or inference models running simultaneously.
• PCIe 3.0 x16 Interface: Delivers 16 GB/s theoretical bandwidth—more than adequate for video frame streaming. No need for PCIe 4.0 infrastructure; standard enterprise server motherboards support this natively without bottleneck.
• Single-Slot, Low-Profile Form Factor (2.713 in H × 6.137 in L): Fits dense 1U/2U server chassis without requiring blank-slot adapters or rear-bracket reconfiguration. Thermal solution is active—no passive heatsink fan-out noise issues in server closets. 50W max power draw means standard 500–650W server PSUs handle multiple cards without strain.
• Four Mini DisplayPort 1.4 Outputs (4 × mDP): Support up to 4 simultaneous 5120 × 2880 @ 60 Hz displays—overkill for most surveillance deployments, but useful if you need dedicated transcoding card diagnostics displays or if the analytics server doubles as a proof-of-concept demo station.
• Compute API Support (CUDA, DirectCompute, OpenCL): Full compatibility with NVIDIA's ecosystem—DeepStream SDK, TensorRT, RAPIDS, and open-source frameworks (PyTorch, TensorFlow). DirectX 12.0 and OpenGL 4.6 support allows integration with Windows-based video management systems that use GPU acceleration for rendering or real-time analytics overlays.

Integration and Compatibility

The VCNT10008GB-LLP integrates into any standard enterprise server with a free PCIe 3.0 x16 slot and Linux or Windows Server support. No external power connectors required—all 50W draw comes through the PCIe bus. Driver support spans NVIDIA CUDA Compute Capability 6.1+ frameworks; deployment with Milestone XProtect, Genetec Security Center, or open-source VMS platforms (ZoneMinder, Frigate, MotionEye) is straightforward once you provision the analytics container or transcoding service. Datasheet and driver downloads are available directly from NVIDIA's website; integration documentation for common video frameworks is maintained in the NVIDIA DeepStream developer community.

Typical Deployment Scenarios

Surveillance Analytics Server: Install one VCNT10008GB-LLP into a 1U dual-socket Xeon server alongside 64–128GB system RAM. Route 10–20 camera streams (via RTSP/RTMP) into a DeepStream pipeline; the GPU processes object detection, tracking, and metadata generation at 25–30 fps per stream.

Video Transcoding Node: Archive high-bitrate camera feeds (4K or 1080p 30 fps) to long-term storage; the GPU accelerates H.265 or H.264 re-encoding, reducing storage footprint by 40–60% compared to software transcoding alone.

Mixed Inference Workload: Run multiple concurrent models (person detection, vehicle classification, license-plate recognition) on different camera zones, multiplexing the 896 CUDA cores across parallel streams.

What's in the Box

The VCNT10008GB-LLP ships as a bare compute card. No cables, no brackets, no thermal compound, no documentation included in the retail package. Assume you supply your own standard PCIe slot clearance, server power delivery, and NVIDIA CUDA driver installation media.

Frequently Asked Questions

Q: Is the VCNT10008GB-LLP (also searched as VCNT10008GB LLP) suitable for real-time 4K video analytics?

A: Yes, but with caveats. A single VCNT10008GB-LLP can process 4–6 concurrent 4K streams at 15–20 fps through a lightweight object detection model (YOLO, MobileNet). For 30 fps 4K across many streams, you need either multiple cards, a higher-tier GPU (RTX 4500 or above), or careful model optimization (quantization, pruning) to stay within the 2.5 TFLOP budget.

Q: What's the memory bandwidth, and does it impact frame throughput?

A: 160 GB/s. For typical video preprocessing (color space conversion, resizing) and inference on frames up to 4K, this is plenty. H.265 decoding and encoding operations pull 20–40 GB/s depending on bitrate and resolution; you won't hit saturation on a single card.

Q: Can I use the VCNT10008GB-LLP in a fanless or passive-cooled server?

A: No. The card has an active thermal solution (fan-cooled heatsink). It requires chassis airflow. Server fan speeds will increase under sustained load; ensure your data center can accommodate noise and thermal output.

Q: Does this card require external power connectors?

A: No. All 50W power comes through the PCIe bus connector. Standard enterprise server PSUs (500W or larger) can handle one or more VCNT10008GB-LLP cards without additional wiring.

Q: What's the driver and OS support?

A: NVIDIA CUDA Compute Capability 6.1. Drivers available for Windows Server 2016+, CentOS 7+, Ubuntu 16.04+, and other Linux distributions. Check NVIDIA's official website for the exact driver version compatible with your OS and CUDA toolkit.

Q: Can I use the VCNT10008GB-LLP for machine learning training, or is it inference-only?

A: Both. 8GB GDDR6 is tight for large model training, but inference at scale and smaller fine-tuning tasks are viable. For production video analytics (where you're running a pre-trained model on live frames), this card is well-suited.

Karl Wilson

I've deployed the PNY VCNT10008GB-LLP into surveillance analytics servers running NVIDIA DeepStream and TensorRT. The 160 GB/s memory bandwidth is the real workhorse here—it keeps frame data flowing into the inference pipeline without CPU bottlenecks, and the 8GB GDDR6 buffer is enough for a dozen concurrent 1080p streams or 4–6 4K feeds running object detection simultaneously. The single-slot form factor and 50W budget make it painless to slide into existing 1U/2U server chassis without rewiring or HVAC drama.

Technical Highlights:

• 2.5 TFLOPs Single-Precision Performance: Enough to handle real-time object detection (YOLO, ResNet) on 10–20 concurrent streams at acceptable latency. Transcoding a single 4K stream to H.265 burns roughly 1–1.5 TFLOP, so you have headroom for inference overlays or parallel analytics tasks without frame drops.
• 160 GB/s Memory Bandwidth: Eliminates the memory-bus bottleneck that plagued earlier compute cards. Video frame preprocessing, model inference input/output, and post-processing metadata assembly all stay under 80 GB/s on typical deployments, leaving safe margin for burst operations.
• PCIe 3.0 x16 (16 GB/s): Sufficient for RTSP stream ingest and metadata egress; even if you're pulling 8 simultaneous 1080p H.265 streams from network cameras, peak bus load stays under 4 GB/s. No PCIe bottleneck—CPU and GPU stay synchronized.

Deployment Considerations:

• The active thermal solution is non-negotiable. If your server closet is already thermally constrained or lacks redundant cooling, a VCNT10008GB-LLP will push fan speeds up 30–40% under sustained load. Plan accordingly.
• 8GB GDDR6 works for inference-scale deployments, but if you're prototyping large custom models or running multiple heavy frameworks simultaneously, you'll hit memory pressure. Consider a second card or a higher-tier GPU (RTX 4500, 6000) if your analytics roadmap includes model complexity growth.

This card is the right fit for mid-market surveillance deployments (30–50 cameras, 10–15 camera feeds processed concurrently for analytics) running on-premise edge inference. It punches above its weight in cost-per-TFLOP and fits standard server hardware without headaches.