NVIDIA A10 VS NVIDIA T4
Choosing between **A10** and **T4** depends on your specific AI workload requirements. The **A10** 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 **$0.40/h** and **$0.11/h** respectively across 51 providers.
📊 Detailed Specifications Comparison
| Specification | A10 | T4 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Turing | - |
| Process Node | 8nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Single-slot PCIe | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 24GB | 16GB | +50% |
| Memory Type | GDDR6 | GDDR6 | - |
| Memory Bandwidth | 600 GB/s | 320 GB/s | +88% |
| Memory Bus Width | 384-bit | 256-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 9,216 | 2,560 | +260% |
| Tensor Cores (AI) | 288 | 320 | -10% |
| RT Cores (Ray Tracing) | 72 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 31.2 TFLOPS | 8.1 TFLOPS | +285% |
| FP16 (Half Precision) | 62.4 TFLOPS | 65 TFLOPS | -4% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 150W | 70W | +114% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A10
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A10 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 T4
Based on current cloud pricing, the T4 starts at a lower hourly rate.
Technical Deep Dive: A10 vs T4
This is a generational comparison within the NVIDIA ecosystem, pitting Ampere against Turing. The A10 has a significant **8GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **T4** is currently about **73% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA A10 is Best For:
- AI inference
- Cloud gaming
- Heavy LLM training
NVIDIA T4 is Best For:
- AI inference
- Video transcoding
- Large model training
Frequently Asked Questions
Which GPU is better for AI training: A10 or T4?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The A10 offers 24GB of GDDR6 memory with 600 GB/s bandwidth, while the T4 provides 16GB of GDDR6 with 320 GB/s bandwidth. For larger models, the A10's higher VRAM capacity gives it an advantage.
What is the price difference between A10 and T4 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, A10 starts at $0.40/hour while T4 starts at $0.11/hour. This represents a 264% price difference.
Can I use T4 instead of A10 for my workload?
It depends on your specific requirements. If your model fits within 16GB of VRAM and you don't need the additional throughput of the A10, the T4 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the A10's architecture may be essential.
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