Specifications Compared
| Spec | A100 | V100 |
|---|---|---|
| TDP | 400W | 300W |
| VRAM | 40-80 GB | 16-32 GB |
| CUDA Cores | 6,912 | 5,120 |
| Memory Type | HBM2e | HBM2 |
| Architecture | Ampere | Volta |
| Form Factors | SXM4, PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink, PCIe 3.0 |
| Tensor Cores | 432 | 640 |
| FP16 Performance | 312 TFLOPS | 125 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 15.7 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 7.8 TFLOPS |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 900 GB/s |
Performance Analysis
Raw specifications reveal stark performance disparities between the A100 SXM4 80GB and V100 16GB, particularly in compute throughput. The A100 delivers 312 TFLOPS in FP16 operations compared to 125 TFLOPS on the V100, accelerating mixed-precision training by up to 2.5 times. FP32 performance edges forward too: 19.5 TFLOPS on A100 versus 15.7 TFLOPS on V100, benefiting single-precision scientific simulations.
Memory capacity and bandwidth profoundly impact real-world workloads. The A100's 80 GB HBM2e supports batch sizes infeasible on the V100's 16 GB HBM2, such as loading large language models without excessive swapping. Its 2039 GB/s bandwidth versus 900 GB/s reduces data transfer bottlenecks, enabling 2.3 times faster memory access for inference on high-resolution inputs or multi-GPU scaling via NVLink.
Power draw differs at 400W TDP for A100 against 300W for V100, implying higher density needs cooling considerations. These metrics translate to shorter training epochs on A100 for transformer models, while V100 suffices for lighter inference serving smaller payloads.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 80GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 397GB Storage | Slovenia | $0.73/GPU/hr | 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 | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 126GB RAM 1114GB Storage | Czechia | $1.00/GPU/hr $2.00/hr total (2×) | Available | ||
Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 646GB Storage | Czechia | $1.07/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 V100 16GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Texas | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | New York City | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Available |
When to Choose the A100 SXM4 80GB
Opt for the A100 SXM4 80GB in demanding machine learning pipelines requiring massive scale. Its 80 GB VRAM accommodates full-parameter fine-tuning of models exceeding 30 billion parameters, impossible on V100's 16 GB without model parallelism hacks. The 312 TFLOPS FP16 rate cuts training times for large language models by factors aligning with its 2.5x uplift over V100's 125 TFLOPS.
Cloud deployments prioritizing throughput over cost favor A100, especially with NVLink interconnects for multi-GPU clusters handling 2039 GB/s bandwidth per card.
When to Choose the Tesla V100 16GB
Select the V100 16GB for cost-sensitive legacy applications or entry-level inference. At $0.10 per hour starting price (average $0.81), it undercuts A100's $0.67 minimum by over 85 percent, ideal for prototyping or serving models under 7 billion parameters fitting within 16 GB HBM2.
Environments with PCIe 3.0 constraints or 300W TDP limits benefit from V100's compatibility, maintaining viability for scientific computing where 15.7 TFLOPS FP32 suffices without Ampere's overhead.
Use Cases
A100's 80 GB VRAM and 312 TFLOPS FP16 enable full-model training on large LLMs without sharding, unlike V100's 16 GB limit. Bandwidth of 2039 GB/s supports massive batch sizes for faster convergence.
Higher 312 TFLOPS FP16 and 80 GB capacity handle batched high-concurrency requests for models over 13B parameters. V100 struggles with memory for production-scale deployments.
A100 fits parameter-efficient fine-tuning datasets in 80 GB HBM2e, with 19.5 TFLOPS FP32 for stable gradients. V100's 16 GB often requires gradient checkpointing overhead.
2039 GB/s bandwidth accelerates diffusion steps on high-resolution images, leveraging 312 TFLOPS for 4x faster generation than V100's 125 TFLOPS.
V100's 15.7 TFLOPS FP32 handles many simulations cost-effectively at $0.10/hr; A100's 19.5 TFLOPS suits memory-intensive CFD with 80 GB VRAM.
Frequently Asked Questions
What is the VRAM difference between A100 SXM4 80GB and V100 16GB?▾
The A100 provides 80 GB HBM2e, five times the V100's 16 GB HBM2. This allows A100 to load larger models without offloading. Bandwidth reaches 2039 GB/s on A100 versus 900 GB/s on V100.
How do FP16 performance numbers compare for A100 vs V100?▾
A100 achieves 312 TFLOPS FP16, 2.5 times the V100's 125 TFLOPS. This boosts deep learning training speed significantly. FP32 is 19.5 TFLOPS on A100 against 15.7 TFLOPS on V100.
What are current cloud prices for these GPUs?▾
A100 SXM4 80GB starts at $0.67 per hour, averaging $1.41 across 24 offers. V100 16GB begins at $0.10 per hour, averaging $0.81 across 25 offers. Prices fluctuate by provider.
Is A100 or V100 better for LLM training?▾
A100 excels with 80 GB VRAM and 312 TFLOPS FP16 for large models. V100's 16 GB limits scale on modern LLMs. Training times drop substantially on A100.
What are the power and form factor differences?▾
A100 has 400W TDP in SXM4 form; V100 uses 300W in SXM2 or PCIe. A100 supports PCIe 4.0 and advanced NVLink. Both offer PCIe variants.
Can V100 still be used for inference in 2024?▾
Yes, V100 handles inference for models under 7B parameters within 16 GB. Its $0.10/hr pricing suits low-volume serving. A100 is preferable for high throughput.
Which is cheaper to rent, the A100 or the V100?▾
Cloud rental prices for both the A100 and V100 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 V100?▾
The A100 has 40 to 80 GB of HBM2e memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find A100 and V100 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 V100?▾
The A100 uses the Ampere architecture (2020) while the V100 uses Volta (2017). The A100 delivers 2.5x the FP16 throughput and 2.3x the memory bandwidth of the V100.