New GPU for Tesla S1070 Tesla S870 Tesla T4 Graphic Card Deep Learning High-performance Computing GPU
Below is a comparison table for the Tesla S1070, Tesla S870, and Tesla T4 graphics cards. These GPUs are part of NVIDIA’s Tesla series, designed for high-performance computing (HPC), data centers, and GPU-accelerated workloads. The Tesla T4 is a much newer and more advanced card compared to the older S1070 and S870.
| Feature/Specification | Tesla S1070 | Tesla S870 | Tesla T4 |
|---|---|---|---|
| Architecture | Tesla | Tesla | Turing |
| Release Year | 2008 | 2007 | 2018 |
| CUDA Cores | 960 (4x 240) | 128 | 2560 |
| FP32 Performance | 4.14 TFLOPS | 0.5 TFLOPS | 8.1 TFLOPS |
| FP64 Performance | 345 GFLOPs | 40 GFLOPs | 0.25 TFLOPS |
| Memory Size | 16 GB (4x 4 GB GDDR3) | 1.5 GB GDDR3 | 16 GB GDDR6 |
| Memory Interface | 512-bit (per GPU) | 384-bit | 256-bit |
| Memory Bandwidth | 408 GB/s (total) | 76.8 GB/s | 320 GB/s |
| ECC Memory Support | No | No | Yes |
| TDP (Thermal Design Power) | 800 W (entire system) | 171 W | 70 W |
| Form Factor | 1U Rackmount System | Full-height, double-width | Low-profile, single-slot |
| Cooling | Passive (system-level cooling) | Passive | Active (blower-style) |
| DirectX Support | N/A (Compute-focused) | N/A (Compute-focused) | N/A (Compute-focused) |
| OpenCL Support | Yes | Yes | Yes |
| CUDA Support | Yes | Yes | Yes |
| Interface | PCIe 2.0 x16 | PCIe 1.0 x16 | PCIe 3.0 x16 |
| Tensor Cores | No | No | Yes (320 Tensor Cores) |
| Ray Tracing Cores | No | No | No |
| Use Case | HPC, Scientific Computing | Early GPU Computing | AI Inference, Deep Learning, HPC |
Tesla S1070
- Architecture: Tesla (pre-Fermi)
- Configuration: 4x Tesla C1060 GPUs in a 1U server form factor.
- CUDA Cores: 960 (240 cores per GPU)
- Total Memory: 16 GB GDDR3 (4 GB per GPU)
- Memory Interface: 512-bit per GPU
- Compute Performance: 4.14 TeraFLOPS (single precision), 330 GigaFLOPS (double precision)
- Power Consumption: 700 W (for the entire system)
- Cooling: Passive (requires external cooling in server environments)
- Use Case: High-performance computing, scientific simulations, and early GPU-accelerated workloads.
- Features: Designed for data centers, it provides massive parallel processing power for its time.
Tesla S870
- Architecture: Tesla (pre-Fermi)
- Configuration: 4x Tesla C870 GPUs in a 1U server form factor.
- CUDA Cores: 512 (128 cores per GPU)
- Total Memory: 6 GB GDDR3 (1.5 GB per GPU)
- Memory Interface: 384-bit per GPU
- Compute Performance: 2.08 TeraFLOPS (single precision), 312 GigaFLOPS (double precision)
- Power Consumption: 700 W (for the entire system)
- Cooling: Passive (requires external cooling in server environments)
- Use Case: Early GPU computing, research, and development.
- Features: One of the first-generation Tesla computing systems, aimed at introducing GPU acceleration to HPC and data centers.
Tesla T4
- Architecture: Turing
- CUDA Cores: 2,560
- Tensor Cores: 320 (for AI acceleration)
- RT Cores: 20 (for ray tracing)
- Memory: 16 GB GDDR6
- Memory Interface: 256-bit
- Compute Performance: 8.1 TeraFLOPS (FP32), 65 TeraFLOPS (Tensor Core performance for AI)
- Power Consumption: 70 W (low-power design)
- Cooling: Passive or active cooling options.
- Use Case: AI inference, deep learning, cloud computing, and graphics acceleration.
- Features:
- Turing architecture with Tensor Cores and RT Cores.
- Optimized for AI, machine learning, and inference workloads.
- Supports mixed-precision computing for improved performance.
- Ideal for data centers and edge computing due to its low power consumption.
Reviews
There are no reviews yet.