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
FP16 performance stands out as the largest gap: the A100 achieves 312 TFLOPS, more than double the V100's 125 TFLOPS, accelerating mixed-precision training in deep learning by enabling faster iterations on large datasets. FP32 performance remains close at 19.5 TFLOPS for the A100 versus 15.7 TFLOPS for the V100, suiting traditional scientific computing where single-precision suffices. This FP16 advantage translates to real-world training speedups of 2-3x in frameworks like PyTorch for transformer models.
Memory bandwidth of 2039 GB/s in the A100, over twice the V100's 900 GB/s, supports larger batch sizes without bottlenecks: for instance, training with batch size 128 on A100 avoids out-of-memory errors common on V100 at batch size 64 for 30B parameter models. The A100's 40 GB HBM2e VRAM exceeds the V100's 32 GB HBM2, allowing inference on models up to 70B parameters with quantization, versus 13B on V100. Higher 400W TDP reflects this capability, demanding robust cooling in clusters.
Inference benefits from bandwidth for high-throughput serving: A100 handles 2x requests per second in batch inference compared to V100 due to faster data movement.
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 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 | ||
 Denvr | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) | |||
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 32GB
| 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 40GB
Choose the A100 SXM4 40GB for workloads demanding high memory capacity and speed: its 40 GB VRAM fits large language models exceeding 32 GB, such as Llama 70B during fine-tuning. The 312 TFLOPS FP16 performance cuts training time by over 2x versus V100's 125 TFLOPS, ideal for iterative AI development.
Multi-node scaling favors A100 with PCIe 4.0 and InfiniBand: it achieves 2x faster inter-GPU communication than V100's PCIe 3.0, suiting distributed training on clusters.
When to Choose the Tesla V100 32GB
Opt for the Tesla V100 32GB in cost-sensitive scenarios: at $0.29/hr from providers versus A100's $1.00/hr, it delivers value for smaller models under 20 GB VRAM. FP32 at 15.7 TFLOPS matches many simulation tasks adequately, with 300W TDP easing power budgets.
Legacy codebases or PCIe deployments benefit from V100's broad availability across 44 cloud offers, avoiding A100's scarcity.
Use Cases
A100's 40 GB VRAM and 312 TFLOPS FP16 handle large models like GPT-3 scale, avoiding OOM errors on V100's 32 GB. Bandwidth of 2039 GB/s doubles V100's 900 GB/s for bigger batches.
A100 supports high-throughput serving with 2039 GB/s bandwidth, processing 2x more requests than V100's 900 GB/s. 40 GB VRAM fits unquantized large models.
312 TFLOPS FP16 accelerates fine-tuning by 2.5x over V100's 125 TFLOPS. Extra 8 GB VRAM enables larger context lengths.
40 GB VRAM generates high-res images at batch size 8, versus V100's limit at 4 with 32 GB. FP16 speed yields 2x faster iterations.
V100's 15.7 TFLOPS FP32 suffices for many simulations at lower $1.01/hr cost. A100's 19.5 TFLOPS edges out for bandwidth-intensive CFD.
Frequently Asked Questions
Is A100 faster than V100?▾
Yes, A100 delivers 312 TFLOPS FP16 versus V100's 125 TFLOPS, a 2.5x gain for ML training. Memory bandwidth reaches 2039 GB/s on A100, more than double V100's 900 GB/s.
A100 vs V100 VRAM comparison?▾
A100 SXM4 40GB offers 40 GB HBM2e, exceeding V100 32GB's 32 GB HBM2. This enables larger models without splitting batches.
Which is cheaper A100 or V100 cloud rental?▾
V100 starts at $0.29/hr averaging $1.01/hr across 44 offers, versus A100's $1.00/hr average $2.63/hr on 5 offers. V100 suits budgets.
A100 power consumption vs V100?▾
A100 TDP is 400W, higher than V100's 300W. This supports greater performance but requires better cooling.
Can V100 run modern LLMs?▾
V100 handles up to 13B parameter LLMs with 32 GB VRAM, but struggles beyond due to memory limits. A100's 40 GB fits 30B+ models.
A100 interconnect better than V100?▾
A100 uses PCIe 4.0 and InfiniBand with NVLink, doubling PCIe 3.0 bandwidth of V100. This boosts multi-GPU scaling.
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.