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Below is a comparison table for the NVIDIA Tesla K80, Tesla K40, Tesla K20, and Tesla K10 GPUs. These GPUs are designed for high-performance computing (HPC), machine learning, and other compute-intensive tasks. Note that the Tesla K10 is a dual-GPU card, while the others are single-GPU cards.
| Feature | Tesla K80 | Tesla K40 | Tesla K20 | Tesla K10 |
|---|---|---|---|---|
| Architecture | Kepler (GK210) | Kepler (GK110B) | Kepler (GK110) | Kepler (GK104) |
| Release Year | 2014 | 2013 | 2012 | 2012 |
| GPU Configuration | Dual-GPU (2x GK210) | Single-GPU | Single-GPU | Dual-GPU (2x GK104) |
| CUDA Cores | 4992 (2496 per GPU) | 2880 | 2496 | 3072 (1536 per GPU) |
| FP32 Performance | 8.74 TFLOPS (total) | 5.04 TFLOPS | 3.52 TFLOPS | 4.58 TFLOPS (total) |
| FP64 Performance | 2.91 TFLOPS (total) | 1.68 TFLOPS | 1.17 TFLOPS | 0.19 TFLOPS (total) |
| Memory | 24 GB GDDR5 (12 GB per GPU) | 12 GB GDDR5 | 5 GB GDDR5 | 8 GB GDDR5 (4 GB per GPU) |
| Memory Bandwidth | 480 GB/s (240 GB/s per GPU) | 288 GB/s | 208 GB/s | 320 GB/s (160 GB/s per GPU) |
| Memory Interface | 384-bit (per GPU) | 384-bit | 320-bit | 256-bit (per GPU) |
| TDP (Thermal Design Power) | 300 W | 235 W | 225 W | 225 W |
| Cooling | Passive | Passive | Passive | Passive |
| Form Factor | Dual-slot, full-height, full-length | Dual-slot, full-height, full-length | Dual-slot, full-height, full-length | Dual-slot, full-height, full-length |
| Use Case | HPC, Deep Learning, Data Analytics | HPC, Deep Learning | HPC, Scientific Computing | Graphics, HPC |
| ECC Memory Support | Yes | Yes | Yes | Yes |
| NVLink Support | No | No | No | No |
NVIDIA Tesla K80
The Tesla K80 is a high-performance dual-GPU accelerator based on NVIDIA’s Kepler architecture (GK210). Released in 2014, it was designed for demanding computational tasks such as deep learning, data analytics, and scientific simulations. The K80 features two GPUs on a single board, each with 12 GB of GDDR5 memory (24 GB total), providing a combined memory bandwidth of 480 GB/s. With 4,992 CUDA cores (2,496 per GPU), it delivers up to 8.74 TFLOPS of FP32 performance and 2.91 TFLOPS of FP64 performance, making it one of the most powerful GPUs of its time. The K80 supports ECC memory for error correction, ensuring data integrity in critical applications. Its passive cooling design and dual-slot form factor make it suitable for data center deployments.
NVIDIA Tesla K40
The Tesla K40 is a single-GPU accelerator based on the Kepler architecture (GK110B). Released in 2013, it was targeted at HPC and deep learning workloads. With 2,880 CUDA cores and 12 GB of GDDR5 memory, the K40 offers a memory bandwidth of 288 GB/s. It delivers up to 5.04 TFLOPS of FP32 performance and 1.68 TFLOPS of FP64 performance, making it a strong choice for scientific computing and machine learning. The K40 also supports ECC memory and features a passive cooling design, making it ideal for data center environments. It was widely used in research and enterprise applications due to its balance of performance and power efficiency.
NVIDIA Tesla K20
The Tesla K20 is a single-GPU accelerator based on the Kepler architecture (GK110). Released in 2012, it was designed for scientific computing and HPC workloads. With 2,496 CUDA cores and 5 GB of GDDR5 memory, the K20 provides a memory bandwidth of 208 GB/s. It delivers up to 3.52 TFLOPS of FP32 performance and 1.17 TFLOPS of FP64 performance, making it well-suited for applications requiring high double-precision compute performance. The K20 supports ECC memory and features a passive cooling design, ensuring reliability in data center environments. It was particularly popular in academic and research institutions for its computational capabilities.
NVIDIA Tesla K10
The Tesla K10 is a dual-GPU accelerator based on the Kepler architecture (GK104). Released in 2012, it was designed for graphics-intensive and lighter HPC workloads. The K10 features two GPUs on a single board, each with 4 GB of GDDR5 memory (8 GB total), providing a combined memory bandwidth of 320 GB/s. With 3,072 CUDA cores (1,536 per GPU), it delivers up to 4.58 TFLOPS of FP32 performance. However, its FP64 performance is significantly lower at 0.19 TFLOPS, making it less suitable for double-precision workloads. The K10 supports ECC memory and features a passive cooling design, making it a cost-effective solution for applications that prioritize single-precision performance.
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