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Dedicated Server Hosting with NVIDIA Tesla GPU
Dedicated servers equipped with professional NVIDIA Tesla GPU cards are ideal options for AI, Deep Learning, and HPC - configurable with up to 2x GPUs for massively parallel computing. Find the right NVIDIA Tesla GPU server for your workload.
The Nvidia Tesla K80 is a server solution from 2014. It has 24 GB of RAM split between 2 cards and a 2946 CUDA core, and all this is great for AI and Deep Learning projects.
Count on NVIDIA Tesla K40 GPU Accelerators to solve your most demanding HPC and big-data challenges. They feature 1.4 TFLOPS performance, 12 GB memory, and ultra-fast 288 GB/s throughput, giving you the power to process large datasets while delivering up to 10X acceleration compared to CPUs.
Specs | K80 | K40c | M40 | A30 PCIe | A40 PCIe | A100 SXM4 80 GB | V100 PCIe 16 GB |
---|---|---|---|---|---|---|---|
GPU Architecture | Kepler 2.0 | Kepler | Maxwell 2.0 | Ampere | Ampere | Ampere | Volta |
CUDA Cores | 4992 | 2880 | 3072 | 3584 | 10752 | 6912 | 5120 |
Core clock speed | 562 MHz | 745 MHz | 948 MHz | 930 MHz | 1305 MHz | 1275 MHz | 1246 MHz |
Boost clock speed | 824 MHz | 876 MHz | 1114 MHz | 1440 MHz | 1740 MHz | 1410 MHz | 1380 MHz |
FP16 performance | -- | -- | -- | 10.32 TFLOPS | 37.42 TFLOPS | 77.97 TFLOPS | 28.26 TFLOPS |
FP32 performance | 8.73 TFLOPS | 5.05 TFLOPS | 6.84 TFLOPS | 10.32 TFLOPS | 37.42 TFLOPS | 19.49 TFLOPS | 14.13 TFLOPS |
FP64 performance | 2.91 TFLOPS | 1.68 TFLOPS | 0.21 TFLOPS | 5.161 TFLOPS | 0.58 TFLOPS | 9.746 TFLOPS | 7.07 TFLOPS |
Memory | 24GB GDDR5 with ECC | 12GB GDDR5 | 12 GB GDDR5 | 24 GB HBM2e | 48 GB GDDR6 with ECC | 80 GB HBM2e | 16 GB HBM2 |
Memory Bandwidth | 480 GB/s | 288 GB/s | 288 GB/s | 933.1 GB/s | 696 GB/s | 2039 GB/s | 900 GB/s |
Memory Bus Width | 384 bit | 384 bit | 384 bit | 3072 bit | 384 bit | 5120 bit | 4096 Bit |
Memory Clock Speed | 5012 MHz | 6008 MHz | 6008 MHz | 1215 MHz | 1812 MHz | 1593 MHz | 1758 MHz |
TDP | 300W | 230W | 250W | 165W | 300W | 400W | 250W |
System Interface | PCIe 3.0x16 | PCIe 3.0x16 | PCIe 3.0x16 | PCIe 4.0 x16 | PCIe 4.0 x16 | PCIe 4.0 x16 | PCIe 3.0 x16 |
DirectX | 12 (11_1) | 12 (11_0) | 12 (12_1) | N/A | 12 (12_2) | N/A | 12.0 |
Shader Model | 5.1 | 5.1 | 6.4 | N/A | 6.7 | N/A | 5.0 |
OpenGL | 4.6 | 4.6 | 4.6 | N/A | 4.6 | N/A | 4.6 |
OpenCL | 1.2 | 1.2 | 1.2 | 3.0 | 3.0 | 3.0 | 2.0 |
CUDA | 3.7 | 3.5 | 5.2 | 8.0 | 8.6 | 8.0 | 7.0 |
Outputs | No outputs | No outputs | No outputs | No outputs | 3x DisplayPort 1.4a | No outputs | No outputs |
Release date | Nov 17th, 2014 | Oct 8th, 2013 | Nov 10th, 2015 | Apr 12th, 2021 | Oct 5th, 2020 | Nov 16th, 2020 | Jun 21st, 2017 |
What Nvidia is happy to talk about is the growing gap in floating point performance and memory bandwidth between CPUs and Nvidia Tesla series GPU cards, which is illustrated on the left. Thus far, over 280 applications in modeling, simulation, data analytics, machine learning, and other workloads have been ported to the CUDA environment so Nvidia Tesla series cards can accelerate them.
Massively Multi-threaded Computing Architecture
NVIDIA GPU Computing Drivers
Supercomputing Performance
Multi-GPU Computing
RTX A5000 Hosting
Achieve an excellent balance between function, performance, and reliability. Assist designers, engineers, and artists to realize their visions.
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Learn MoreLeave us a note when purchasing, or contact us to apply a trial GPU server. You have enough time to test the performance, network latency, compatibility, multiple instance capacity, etc.
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