Technical Snapshot

The Gigabyte GeForce RTX 4080 SUPER OC 16GB is a gaming desktop graphics card built on the NVIDIA Ada Lovelace architecture (AD103 GPU). It delivers 10,240 CUDA cores, 16 GB GDDR6X memory, on a 256-bit bus, boost clock of 2,595 MHz. The card connects via PCIe 4.0 x16 and draws 320W TDP, requiring a 750W minimum power supply. Part of the Gigabyte OC product line, this card targets 4K/8K gaming, AI/ML inference, and professional rendering.

Use Case
4K/8K gaming, AI/ML inference, real-time ray tracing, 3D rendering, content creation
Compatibility
Requires PCIe x16 slot, 750W PSU, adequate case clearance
Category
Desktop Graphics Card — GeForce RTX 4080 SUPER (Ada Lovelace)
Gigabyte GeForce RTX 4080 SUPER OC 16GB
Gigabyte GPU
Gigabyte OC

GeForce RTX 4080 SUPER OC 16GB

Ada Lovelace · AD103 · 10,240 CUDA Cores · 16 GB GDDR6X · PCIe 4.0 x16

Total number of CUDA parallel processing cores. Higher count enables faster rendering, AI inference, and compute workloads.
CUDA Cores 10,240
Video memory capacity and type. More VRAM allows higher-resolution textures, larger AI models, and multi-monitor setups.
VRAM 16 GB GDDR6X
Maximum GPU frequency under load. Higher boost clocks improve frame rates and compute throughput.
Boost Clock 2,595 MHz
Total board power consumption. Ensure your PSU has adequate wattage for stable operation.
TDP 320W
MPNGV-N408SAERO OC-16GD
EAN889523041352
CategoryGraphics Cards
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GPU Core & Memory

GPU Chip NVIDIA AD103
Architecture Ada Lovelace
CUDA Cores 10,240
Base Clock 2,295 MHz
Boost Clock 2,595 MHz
VRAM 16 GB GDDR6X
Memory Bus 256-bit
PCIe Interface PCIe 4.0 x16
TDP 320W
Recommended PSU 750W
Cooling Dual fan
Display Outputs 3x DP 1.4a, 1x HDMI 2.1

Full Specifications

Data sourced from Gigabyte product specifications and NVIDIA reference data
GPU
GPU ChipNVIDIA AD103
ArchitectureAda Lovelace
CUDA Cores10,240
Base Clock2,295 MHz
Boost Clock2,595 MHz
GPU VendorNVIDIA
Memory
VRAM16 GB GDDR6X
Memory Bus256-bit
Interface & Power
PCIe InterfacePCIe 4.0 x16
TDP320W
Recommended PSU750W
Card Design
Slot Width2.5-slot
CoolingDual fan
Display Outputs3x DP 1.4a, 1x HDMI 2.1
Product
BrandGigabyte
Brand SeriesOC
GPU SeriesGeForce RTX 4080 SUPER
SegmentGaming
MPNGV-N408SAERO OC-16GD
EAN889523041352
Physical
Weight3.10 kg
Package Dimensions34.1884 x 15.0114 x 7.493 cm

Support & Downloads

NVIDIA GeForce Game Ready Driver (Latest)Windows 10/11 64-bit
NVIDIA Drivers →
Gigabyte GeForce RTX 4080 SUPER OC 16GB User ManualPDF · Installation guide and specifications
Gigabyte Support →
← Back to GPU Catalog

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Frequently Asked Questions

What motherboards are compatible with the RTX 4080 SUPER OC 16GB?

The Gigabyte GeForce RTX 4080 SUPER OC 16GB uses a PCIe 4.0 x16 interface and is compatible with any motherboard that has a PCIe x16 slot. This includes AMD AM5 and AM4 motherboards as well as Intel LGA 1851 and LGA 1700 boards. PCIe is backward-compatible, so older PCIe 3.0 slots will work at reduced bandwidth.

What power supply is recommended for the RTX 4080 SUPER OC 16GB?

A minimum 750W power supply is recommended for the Gigabyte GeForce RTX 4080 SUPER OC 16GB. The card has a TDP of 320W, so adequate PSU headroom is critical for stable operation under peak load, especially when paired with a high-end CPU.

Will the RTX 4080 SUPER OC 16GB fit in my case?

Based on package dimensions, the Gigabyte GeForce RTX 4080 SUPER OC 16GB requires clearance of approximately 34.1884 cm (342 mm). Verify your case's maximum GPU length specification before purchasing. Also ensure sufficient PCIe slot clearance and adequate airflow.

How much VRAM does the RTX 4080 SUPER OC 16GB have?

The Gigabyte GeForce RTX 4080 SUPER OC 16GB features 16 GB GDDR6X on a 256-bit bus. This is suitable for 4K/8K gaming, large AI/ML models, and professional rendering workloads.

Can the RTX 4080 SUPER OC 16GB be used for AI and machine learning?

Yes. The Gigabyte GeForce RTX 4080 SUPER OC 16GB is built on NVIDIA's Ada Lovelace architecture with 10,240 CUDA cores and 16 GB VRAM, making it capable of AI inference and training workloads. It supports CUDA, Tensor Cores, and NVIDIA's AI frameworks. For dedicated enterprise AI, consider NVIDIA's professional-grade GPUs.

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