Specifications Compared
| Spec | A100 | T4 |
|---|---|---|
| TDP | 400W | 70W |
| VRAM | 40-80 GB | 16 GB |
| CUDA Cores | 6,912 | 2,560 |
| Memory Type | HBM2e | GDDR6 |
| Architecture | Ampere | Turing |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | 320 |
| FP16 Performance | 312 TFLOPS | 8.1 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 8.1 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | 130 TOPS |
| Memory Bandwidth | 2,039 GB/s | 320 GB/s |
Performance Analysis
FP16 performance defines training efficiency: the A100 SXM4 40GB achieves 312 TFLOPS, compared to 8.1 TFLOPS on the T4. This gap accelerates mixed-precision training of deep neural networks on A100 by approximately 38 times. FP32 at 19.5 TFLOPS on A100 also surpasses T4's 8.1 TFLOPS for single-precision tasks common in scientific simulations.
Memory bandwidth impacts batch sizes directly: A100's 2039 GB/s supports larger batches and models versus T4's 320 GB/s, which limits scale in memory-bound workloads like large language model training. Higher bandwidth reduces data transfer bottlenecks, speeding iterations.
For inference, T4's matched FP16 and FP32 at 8.1 TFLOPS pair with 70W TDP for dense deployments, contrasting A100's 400W power draw. T4 suits low-latency serving where full A100 capabilities remain underutilized.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 40GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 2826GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 794GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 557GB Storage | Czechia | $1.00/GPU/hr | Available | ||
 Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
Tesla T4
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 4 vCPU 16GB RAM | Virginia | $0.53/GPU/hr | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 8 vCPU 32GB RAM | Virginia | $0.75/GPU/hr | |||
AWS | 4×NVIDIA Tesla T4 16GB VRAM | 16GB | 48 vCPU 192GB RAM | Virginia | $0.98/GPU/hr $3.91/hr total (4×) | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 16 vCPU 64GB RAM | Virginia | $1.20/GPU/hr | |||
AWS | NVIDIA Tesla T4 16GB VRAM | 16GB | 32 vCPU 128GB RAM | Virginia | $2.18/GPU/hr |
When to Choose the A100 SXM4 40GB
The A100 SXM4 40GB excels in large-scale deep learning training. Its 40 GB HBM2e VRAM accommodates massive models, and 312 TFLOPS FP16 performance cuts training times significantly. NVLink interconnects enable multi-GPU scaling for distributed workloads.
High-performance computing benefits from A100's 2039 GB/s bandwidth and 19.5 TFLOPS FP32, ideal for simulations requiring high throughput.
When to Choose the Tesla T4
The NVIDIA Tesla T4 fits cost-sensitive inference deployments. At $0.53 per hour minimum pricing, it delivers 8.1 TFLOPS FP16 with 16 GB GDDR6 VRAM sufficient for most serving tasks. Low 70W TDP supports high-density servers without excessive cooling costs.
Lightweight fine-tuning or edge AI leverages T4's efficiency, avoiding A100's $1.00 per hour starting price and 400W power demands.
Use Cases
A100's 312 TFLOPS FP16 and 40 GB HBM2e VRAM handle large language models effectively during training. T4's 8.1 TFLOPS and 16 GB limit scale for such tasks.
T4 offers efficient inference at 8.1 TFLOPS FP16 with $0.53 per hour pricing and 70W TDP. It suffices for serving LLMs without A100's overhead.
A100's 2039 GB/s bandwidth and 40 GB VRAM support larger batch sizes in fine-tuning. T4's 320 GB/s constrains complex adaptations.
A100 accelerates image generation with 312 TFLOPS FP16 and high memory capacity. T4 struggles with bandwidth-intensive diffusion models.
A100's 19.5 TFLOPS FP32 outperforms T4's 8.1 TFLOPS for precise simulations. NVLink aids multi-GPU scientific workloads.
Frequently Asked Questions
What is the performance difference in FP16 between A100 SXM4 40GB and T4?▾
A100 delivers 312 TFLOPS FP16, while T4 provides 8.1 TFLOPS. This makes A100 about 38 times faster for mixed-precision AI training.
How much VRAM do A100 SXM4 40GB and T4 have?▾
A100 SXM4 40GB offers 40 GB HBM2e VRAM. T4 has 16 GB GDDR6, limiting it to smaller models.
What are the cloud pricing ranges for these GPUs?▾
A100 SXM4 40GB starts at $1.00 per hour, averaging $2.63 across five offers. T4 begins at $0.53 per hour, averaging $1.66 across six offers.
Which GPU has higher memory bandwidth?▾
A100 achieves 2039 GB/s with HBM2e. T4 reaches 320 GB/s with GDDR6, affecting large batch processing.
What are the TDP values for A100 and T4?▾
A100 SXM4 40GB consumes 400W TDP. T4 uses 70W, enabling denser deployments.
When is T4 preferable over A100?▾
T4 suits inference with its 8.1 TFLOPS FP16/FP32 and low cost. A100 excels in training requiring 312 TFLOPS FP16.
Which is cheaper to rent, the A100 or the T4?▾
Cloud rental prices for both the A100 and T4 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the A100 have compared to the T4?▾
The A100 has 40 to 80 GB of HBM2e memory. The T4 has 16 GB of GDDR6 memory.
Can I find A100 and T4 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the A100 and the T4?▾
The A100 uses the Ampere architecture (2020) while the T4 uses Turing (2018). The A100 delivers 38.5x the FP16 throughput and 6.4x the memory bandwidth of the T4.