Performance Rating
The GeForce RTX 5090 is an enthusiast-class graphics card by NVIDIA, launched on January 30th, 2025. Built on the 5 nm process, and based on the GB202 graphics processor, in its GB202-300-A1 variant, the card supports DirectX 12 Ultimate. This ensures that all modern games will run on GeForce RTX 5090. Additionally, the DirectX 12 Ultimate capability guarantees support for hardware-raytracing, variable-rate shading and more, in upcoming video games. The GB202 graphics processor is a large chip with a die area of 750 mm² and 92,200 million transistors. It features 21760 shading units, 680 texture mapping units, and 176 ROPs. Also included are 680 tensor cores which help improve the speed of machine learning applications. The card also has 170 raytracing acceleration cores. NVIDIA has paired 32 GB GDDR7 memory with the GeForce RTX 5090, which are connected using a 512-bit memory interface. The GPU is operating at a frequency of 2017 MHz, which can be boosted up to 2407 MHz, memory is running at 1750 MHz (28 Gbps effective).
Contents:
Memory ML Performance Compute Power Architecture & Compatibility ML Software Support Clocks & Performance Power Consumption Rendering NVENC / NVDEC Benchmarks AdditionalMemory
Memory Size
Memory Type
Memory Bandwidth
Memory Bus Width
ML Performance
FP16 (Half Precision)
BF16 (Brain Float)
TF32 (TensorFloat)
Compute Power
FP32 (Single Precision)
FP64 (Double Precision)
CUDA Cores
RT Cores
Architecture & Compatibility
GPU Architecture
SM (Streaming Multiprocessor)
PCIe Version
ML Software Support
CUDA Version
Clocks & Performance
Base Clock
Boost Clock
Memory Clock
Power Consumption
TDP/TGP
Recommended PSU
Power Connector
NVENC / NVDEC
NVENC Generation
NVENC Chips
Max Encoding Sessions
Encoding (NVENC)
NVDEC Generation
NVDEC Chips
Decoding (NVDEC)
Benchmarks
llama.cpp, llama 7B Q4_0
llama.cpp, llama-2-7b-Q4_0
Geekbench AI, FP16
Geekbench AI, INT8
Geekbench AI, FP32
Additional
Slots
Release Date
Display Outputs
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NVIDIA
Флагманская GPU на архитектуре Hopper с 80 ГБ памяти HBM3 в форм-факторе SXM. Создана для обучения и инференса больших языковых моделей, HPC задач. Поддерживает FP64, FP32, TF32, FP16, BF16, FP8 и INT8 вычисления с Tensor Cores 4-го поколения.
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