Lenovo 4X61P39479 Graphic_bo RTX 4000 ADA 20GB Graphics

Lenovo 4X61P39479 RTX 4000 Ada Generation 20GB Professional Graphics Card

Overview

The Lenovo 4X61P39479 is a professional-grade discrete graphics card built around NVIDIA's RTX 4000 Ada Generation GPU, pairing 6,144 CUDA cores with 20GB of GDDR6 memory on a 160-bit bus. At 360 GB/s of memory bandwidth, this card is sized for compute-intensive workloads — AI inferencing pipelines, multi-stream video decode, 3D visualization, and simulation workloads that routinely exhaust mainstream GPUs. If you're evaluating workstation-class graphics for high-throughput professional applications, the 4X61P39479 sits in a segment that balances compute density with a single-slot-friendly power envelope relative to consumer-grade equivalents.

Explore the full Lenovo workstation and compute catalog for compatible systems and accessories.

Key Features

• 6,144 CUDA Cores (Ada Lovelace Architecture): The Ada generation delivers materially higher throughput per watt compared to Ampere predecessors. For multi-stream AI inferencing or parallel video analytics, more CUDA cores translate directly to reduced processing latency per frame or per batch.
• 20GB GDDR6 at 360 GB/s: The 20GB frame buffer is a decisive factor for large-model inferencing and high-resolution multi-display rendering. Workloads that would cause VRAM-overflow thrashing on a 16GB card — loading large neural network weights or processing uncompressed 8K frames — fit comfortably in this memory pool. The 360 GB/s bandwidth keeps that memory fed without creating a bottleneck at the bus.
• 160-Bit Memory Bus: Narrower than the 256-bit buses on full-workstation cards like the RTX 4000 SFF's larger siblings, the 160-bit configuration is appropriate for precision AI compute and structured data workloads. Buyers pushing unstructured large-dataset streaming at maximum throughput should verify the bandwidth profile fits their specific access patterns.
• PCIe 4.0 x16 Interface: Full-bandwidth PCIe 4.0 x16 connectivity doubles the host-to-GPU transfer rate versus PCIe 3.0 — relevant when streaming large datasets from NVMe storage directly into GPU memory for inference pipelines or simulation preprocessing.
• 4x DisplayPort Outputs, up to 7680×4320: Four DisplayPort outputs support up to 8K resolution, enabling dense multi-monitor control room layouts or high-resolution visualization walls without a separate display controller. Security operations centers and trading floors running 4+ monitors per workstation can drive the full array from a single card.
• DirectX 12.0 / OpenGL 4.6 / OpenCL 3.0 / Shader Model 6.7: Full API coverage across graphics and compute frameworks means this card integrates into existing professional application stacks — CAD/CAM, simulation engines, and custom GPGPU pipelines — without compatibility gaps. OpenCL 3.0 is particularly relevant for cross-platform compute frameworks used in video analytics middleware.
• Active Cooling (Single Fan): The active single-fan cooler manages thermals without requiring a dual-slot footprint, preserving adjacent PCIe slots in dense workstation chassis. Verify chassis airflow specifications if deploying in rack-mounted workstations with restricted airflow.
• CUDA-Enabled for Professional Compute: Native CUDA support opens access to NVIDIA's full ecosystem of accelerated libraries — cuDNN for deep learning, NVENC/NVDEC for hardware-accelerated video encode/decode, and TensorRT for optimized inference. For video surveillance analytics platforms running deep learning object detection, CUDA acceleration can reduce per-camera CPU load significantly.

Integration & Compatibility

The professional workstation GPU installs into any PCIe 4.0 x16 slot and maintains backward compatibility with PCIe 3.0 systems at reduced bandwidth. The PCIe x16 form factor fits standard ATX and small-form-factor workstation chassis that meet the card's physical length and power delivery requirements — confirm slot clearance and supplemental power connector availability in your target system before ordering.

For AI-accelerated physical security deployments, this card pairs with network video recorders and edge servers running GPU-accelerated VMS analytics. Platforms that offload deep learning inference to a discrete GPU — rather than running inference on CPU — see order-of-magnitude improvements in concurrent-stream capacity. Verify that your VMS or analytics middleware explicitly supports CUDA or NVENC offload and publishes GPU compatibility lists before specifying this card as the inference accelerator.

The card's OpenCL 3.0 and DirectX 12.0 support also positions it for simulation, digital twin, and 3D visualization workloads common in physical security infrastructure planning and warehouse automation design environments. Buyers in those verticals should cross-reference GPU memory requirements against scene complexity in their specific simulation engine.

Frequently Asked Questions

Q: What is the memory capacity and type on the Lenovo 4X61P39479?

A: The 4X61P39479 carries 20GB of GDDR6 memory on a 160-bit bus, delivering up to 360 GB/s of memory bandwidth.

Q: How many displays can the 4X61P39479 drive simultaneously?

A: The card features four DisplayPort outputs and supports a maximum resolution of 7680×4320 (8K), making it suitable for multi-monitor control room or visualization wall deployments.

Q: What PCIe slot does the 4X61P39479 require?

A: It uses a PCIe 4.0 x16 interface. It is backward-compatible with PCIe 3.0 x16 slots at reduced bandwidth.

Q: Does the 4X61P39479 support CUDA for AI and analytics workloads?

A: Yes. The card is CUDA-enabled with 6,144 CUDA cores on the Ada Lovelace architecture, supporting NVIDIA's accelerated compute libraries including cuDNN and TensorRT.

Q: What compute APIs does the 4X61P39479 support?

A: It supports DirectX 12.0, OpenGL 4.6, OpenCL 3.0, and Shader Model 6.7, covering the major professional graphics and compute frameworks.

Q: What cooling solution does the 4X61P39479 use?

A: Active cooling with a single fan. Verify chassis airflow specs for rack-mounted or restricted-airflow deployments.

Marty Allison

The 4X61P39479 lands in a useful spot for integrators building GPU-accelerated analytics servers: 20GB of GDDR6 at 360 GB/s gives you enough frame buffer to run large detection models without VRAM overflow, and the 6,144 CUDA cores on Ada handle parallel inference across a meaningful number of concurrent camera streams. This isn't a card for gaming rigs — it's positioned for professional compute environments where VRAM capacity and API breadth matter more than raw consumer benchmark scores.

Technical Highlights:

• 20GB GDDR6 / 360 GB/s: Large enough to load production-scale detection models (YOLOv8-class and above) without paging. The 360 GB/s bandwidth prevents the memory bus from becoming a bottleneck during parallel batch inference.
• 6,144 CUDA Cores (Ada Lovelace): Ada's architectural improvements over Ampere deliver higher throughput per clock. For multi-stream video analytics workloads, this translates to more concurrent streams per server node compared to equivalent previous-generation parts.
• 4x DisplayPort / 8K Max Resolution: Four outputs at up to 7680×4320 make this viable as a display controller for operations center video walls — eliminating the need for a separate display card in systems that also run compute workloads.

Deployment Considerations:

• Confirm your target chassis provides adequate supplemental PCIe power and slot clearance for this card's physical dimensions before specifying. Active cooling requires sufficient chassis airflow — rack-mounted workstations with high ambient temperatures need airflow verification.
• The 160-bit memory bus is narrower than the 256-bit configurations on higher-tier workstation cards. For workloads dominated by sequential large-block memory access rather than random-access compute, validate that 360 GB/s meets your throughput floor before committing.

Best fit: GPU-accelerated analytics server builds where a large VRAM pool and CUDA ecosystem support are the primary requirements — specifically edge inference nodes for multi-camera AI analytics deployments or professional visualization workstations in security operations centers running dense multi-monitor layouts.