NVIDIA T4 VS NVIDIA Tesla K80
Choosing between **T4** and **K80** depends on your specific AI workload requirements. The **K80** leads in both memory capacity and raw compute power, making it a stronger choice for high-end LLM training. Currently, you can rent these GPUs starting from **$0.11/h** and **$0.10/h** respectively across 12 providers.
📊 Detailed Specifications Comparison
| Specification | T4 | K80 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Turing | Kepler | - |
| Process Node | 12nm | 28nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Single-slot PCIe | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 16GB | 24GB | -33% |
| Memory Type | GDDR6 | GDDR5 | - |
| Memory Bandwidth | 320 GB/s | 480 GB/s | -33% |
| Memory Bus Width | 256-bit | 384-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 2,560 | 4,992 | -49% |
| Tensor Cores (AI) | 320 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 8.1 TFLOPS | 8.7 TFLOPS | -7% |
| FP16 (Half Precision) | 65 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 70W | 300W | -77% |
| PCIe Interface | PCIe 3.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA T4
Higher VRAM capacity and memory bandwidth are critical for training large language models. The K80 offers 24GB compared to 16GB.
AI Inference
NVIDIA T4
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA Tesla K80
Based on current cloud pricing, the K80 starts at a lower hourly rate.
Technical Deep Dive: T4 vs K80
This is a generational comparison within the NVIDIA ecosystem, pitting Turing against Kepler. The K80 has a significant **8GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **K80** is currently about **9% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA T4 is Best For:
- AI inference
- Video transcoding
- Large model training
NVIDIA Tesla K80 is Best For:
- Old software support
- Any modern AI
Frequently Asked Questions
Which GPU is better for AI training: T4 or K80?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The T4 offers 16GB of GDDR6 memory with 320 GB/s bandwidth, while the K80 provides 24GB of GDDR5 with 480 GB/s bandwidth. For larger models, the K80's higher VRAM capacity gives it an advantage.
What is the price difference between T4 and K80 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, T4 starts at $0.11/hour while K80 starts at $0.10/hour. This represents a 10% price difference.
Can I use K80 instead of T4 for my workload?
It depends on your specific requirements. If your model fits within 24GB of VRAM and you don't need the additional throughput of the T4, the K80 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the T4's architecture may be essential.
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