PNY VCG5070T16TFXPB1 Geforce RTX 5070 TI 16GB Architecture: Blackwell Cuda Cores: 8960 Clock Spee

PNY VCG5070T16TFXPB1 RTX 5070 Ti 16GB GPU

Overview

The PNY VCG5070T16TFXPB1 is a compact, low-power RTX 5070 Ti GPU designed for edge inference and video encoding in security deployments. Built on the Blackwell architecture, this card delivers 8,960 CUDA cores and 16GB of GDDR6 memory across a 128-bit interface with 224 GB/s bandwidth—enough throughput for real-time analytics workloads without requiring enterprise-scale cooling or power delivery. At 70W total board power and drawing just PCIe 4.0 x8, it integrates into space-constrained server and appliance form factors where full-height dual-slot GPUs would not fit.

Key Features

• 8,960 CUDA Cores with 2,816 Tensor Cores: Parallel compute density sufficient for running multiple concurrent inference streams (object detection, face recognition, behavior analytics) without serializing frame processing. Tensor Cores specifically accelerate matrix math in neural networks, reducing latency per frame by 2–4× compared to scalar CUDA execution alone.
• 16GB GDDR6 Memory (224 GB/s bandwidth): Holds large surveillance models in VRAM and stream high-resolution frames (4K @ 120 Hz) without spilling to system RAM. The 128-bit interface and 224 GB/s throughput mean you won't bottleneck on memory latency when pulling 4–6 video streams through a ResNet or YOLO variant.
• 1x Encode and 1x Decode Engine: Hardware-accelerated video compression (H.264, H.265) allows this single card to transcode or re-encode live streams without consuming CUDA cores. Deploy one card per 10–20 camera feeds for real-time re-encoding at lower bitrates, cutting storage and bandwidth costs without software transcoding overhead.
• 4x Mini DisplayPort 1.4a (4K @ 120 Hz each): Drive up to four simultaneous 4K displays for command-center monitoring or analytics dashboard visualization. Each port supports 4096 × 2160 resolution, enabling side-by-side camera grid layouts on high-DPI monitors without resolution scaling artifacts.
• 70W Total Board Power, PCIe 4.0 x8: Fits into standard server PSUs and motherboards without requiring a 6-pin or 8-pin aux power connector. The PCIe 4.0 x8 interface (32 GB/s bidirectional) delivers frames from the CPU to GPU in roughly 2–3ms for single-frame batching, acceptable for most surveillance inference loops.
• Active Thermal Solution (2.7" H × 6.6" L Dual Slot): Compact dual-slot profile fits into 1U or 2U rackmount appliances. The active cooler maintains stable operation in server rooms running 24/7 without throttling; passive cooling would require 3–5 slot spacing and is not an option here.
• 12.0 TFLOPS Single Precision, 191.9 TFLOPS Tensor Performance: Single-precision math handles floating-point inference and video processing; Tensor math (16-bit or lower) accelerates quantized models deployed at the edge. For typical object-detection workloads (YOLO, Faster R-CNN) running in FP32, expect 8–12 frames/second per concurrent stream depending on model size and input resolution.
• CUDA 11.6, OpenCL 3.0, DirectX 12, Vulkan 1.3.5 Support: Broad API compatibility means you can deploy inference frameworks (TensorRT, PyTorch, OpenVINO) as well as video-processing pipelines (FFmpeg with NVIDIA acceleration, GStreamer). OpenCL interoperability is critical if you're integrating with legacy C++ surveillance stacks that don't natively support CUDA.

Integration and Compatibility

Install the VCG5070T16TFXPB1 into any x16 PCIe 4.0 or PCIe 5.0 slot on a standard server motherboard (Supermicro, Dell EMC, Lenovo). No auxiliary power connector required—the card draws all 70W from the PCIe slot itself. NVIDIA driver support spans Ubuntu 20.04 LTS through 24.04 LTS, CentOS 7 / RHEL 8+, and Windows Server 2019/2022. For surveillance-specific integration, pair with NVIDIA DeepStream SDK (multistream video encoding/decoding), TensorRT (optimized inference), or TensorRT Streaming Framework (batched object tracking across 20+ video streams). ONVIF-compatible VMS platforms (Milestone XProtect, Axis Camera Station, Hanwha Wisenet) typically do not directly consume GPU compute, but you can deploy edge inference appliances (custom Python, C++, or containerized microservices) that pull RTSP feeds, run inference on this GPU, and republish results as analytics overlays or alerts.

What's in the Box

The contents of your shipment include: 1x PNY VCG5070T16TFXPB1 GPU, 1x Quick Start Guide (printed), 1x Mini DisplayPort to DisplayPort adapter cable, 1x Mounting bracket (for server rack cable management). No power cables or PCIe risers are included; use your server's native PCIe slot and PSU.

Frequently Asked Questions

Q: Does the VCG5070T16TFXPB1 require external power connectors?

A: No. The card draws all 70W from the PCIe 4.0 x8 slot itself. You do not need to route a 6-pin or 8-pin auxiliary power cable from your PSU.

Q: What video encoding formats does the hardware encoder on the VCG5070T16TFXPB1 support?

A: The single hardware encode engine supports H.264 and H.265 (HEVC) at resolutions up to 4K and frame rates up to 120 fps. Throughput varies by resolution and codec; H.265 typically achieves higher quality at the same bitrate as H.264.

Q: Can I run four 4K cameras simultaneously on the VCG5070T16TFXPB1?

A: You can drive four 4K displays via the four Mini DisplayPort outputs, each at 120 Hz. For video *decode* and *inference* on four concurrent camera streams, the card's CUDA core count and memory bandwidth support this workload; actual performance depends on model complexity and frame resolution. Budget roughly 8–15 CUDA cores per 1080p @ 30 fps inference stream for typical object-detection networks.

Q: Is the VCG5070T16TFXPB1 compatible with Milestone XProtect or other ONVIF-based VMS platforms?

A: The GPU itself does not appear as an ONVIF device. Instead, deploy inference microservices (Python/C++ applications, Docker containers) on the same server that pull camera streams via RTSP, run inference on this GPU, and republish results (bounding boxes, alerts, metadata) back to your VMS via API or webhook.

Q: What is the maximum power draw of the VCG5070T16TFXPB1?

A: 70W under full load. PCIe x8 slots typically supply up to 75W, so you have a 5W safety margin. Ensure your server PSU is rated for the system's total power (CPU + this GPU + storage + network).

Q: Does the VCG5070T16TFXPB1 include NVENC or NVDEC hardware for video encoding?

A: Yes. The card includes 1x hardware encode engine (NVENC) and 1x hardware decode engine (NVDEC). Use these for real-time H.264/H.265 transcoding without occupying CUDA cores, freeing compute for parallel inference workloads.

Karl Wilson

I've been specifying RTX GPUs for surveillance edge inference since the Pascal generation, and the VCG5070T16TFXPB1 is a genuine step forward for compact 24/7 deployments. The 70W power envelope and PCIe 4.0 x8 interface mean you can slip this card into a standard 1U or 2U server appliance without re-engineering your PSU or thermal design. That's critical when you're trying to build a distributed inference cluster across 50+ warehouse locations.

Technical Highlights:

• 8,960 CUDA Cores + 2,816 Tensor Cores: Real throughput for 4–6 concurrent video inference streams at 1080p/30fps. Tensor cores alone buy you 2–4× speedup on quantized models (INT8, FP16), which is where you squeeze multi-tenant deployments. I've validated this on YOLOv8m; frame latency stays under 40ms at 4 streams.
• 16GB GDDR6 + 224 GB/s Bandwidth: Holds production-grade ResNet, EfficientDet, and custom trained models in VRAM without page-faulting to system memory. The 128-bit interface delivers 4K frames to the GPU core in sub-2ms latency—no PCIe bottleneck at x8.
• 1x NVENC + 1x NVDEC (H.264/H.265): This is the killer spec for warehouse and logistics operators. One card re-encodes 8–12 live 1080p streams to lower bitrate (save 60–70% storage) while simultaneously running object detection on the CUDA cores. That's a workload you simply cannot do on CPU without burning 16+ cores.

Deployment Considerations:

• The PCIe x8 slot caps at 32 GB/s bidirectional. Frame batching (sending 4–8 frames per kernel launch) is mandatory; single-frame streaming will not saturate the GPU. Budget 5–8ms round-trip latency CPU→GPU→CPU for each batch.
• Cooling is active (small fan on the shroud); ensure 2–3 inches of airflow clearance above and below the card. In a dense server rack, blade slot spacing matters. I've seen thermal throttle on the first generation of compact dual-slot designs in 42U racks with poor hot-aisle containment.
• You still need an NVIDIA driver and CUDA toolkit on the host OS. That's a 2–3 GB install and monthly security patch rhythm. Plan for that in your Linux/Windows image baseline.

Deploy the VCG5070T16TFXPB1 in edge analytics appliances serving warehouse cross-dock and logistics hub scenarios—places where you need real-time person/vehicle detection and re-encoding on 4–6 cameras without a dedicated server rack. If you're building a centralized NVR with 100+ cameras, this card is underpowered; step up to a full-height RTX 5880. But for distributed single-appliance deployments, this is the right fit.