Original Graphics Card For NVIDIA TESLA K80 24GB GPU J0G95A 796124-001 699-22080-0200-501 Video Card
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Brand Name: lotorasia
Model Number: K80
Chipset Manufacturer: NVIDIA
Overclocked: No
Video Memory Type: GDDR5
Video Memory Capacity: 24GB
Memory Interface: 384 BIT
High-concerned chemical: None
Interface Type: PCI Express 3.0x16
Cooler Type: No Fan
Power Connector: 8pin
GPU Model: Tesla K80
Output Interface Type: VGA (D-Sub)
Origin: Mainland China
RGB: No
NVIDIA Tesla K80 is a dual-GPU computing card based on the Kepler architecture, whose main parameters include: Dual GK210 cores, 4992 CUDA cores, 24GB GDDR5 memory (12GB per GPU), 384-bit bandwidth, 480GB/s bandwidth, 824MHz core frequency, 300W power consumption, passive heat dissipation design .
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Youdaoplaceholder0 Core parameters
Youdaoplaceholder0 Architecture and core
It adopts the Kepler architecture and is equipped with two GK210 GPU cores. Physically, it integrates two independent Gpus with 12GB of video memory within one card.
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Each GPU contains 2,496 CUDA cores (totaling 4,992), with a core frequency of 824MHz.
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Youdaoplaceholder0 Memory configuration
The video memory type is GDDR5, with a total capacity of 24GB (12GB per GPU), a bit width of 384 bits, and a bandwidth of 480GB/s.
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Supports ECC error correction and Unified Virtual Address Space (UVA).
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Youdaoplaceholder0 Performance & power
The performance of double-precision floating-point is 2.91 TFLOPs, and that of single-precision is 8.74 TFLOPs.
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The maximum power consumption is 300W. It requires an 8-pin power supply interface and PCIe 3.0 x16 bus.
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Youdaoplaceholder0 Design features
Youdaoplaceholder0 Cooling method : Passive cooling design, relying on server air ducts for cooling, not suitable for ordinary workstations.
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Youdaoplaceholder0 Dual-GPU logic : Must be identified as two separate Gpus during programming and interconnected via a PCIe bridge chip (PLX).
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Youdaoplaceholder0 Application scenarios
Optimized for high-density parallel computing such as machine learning and scientific computing, but due to its outdated architecture (released in 2014), the performance of modern AI tasks is relatively low
