Specifications Compared
| Spec | A100 | B200 |
|---|---|---|
| TDP | 400W | 1000W |
| VRAM | 40-80 GB | 192 GB |
| CUDA Cores | 6,912 | 18,432 |
| Memory Type | HBM2e | HBM3e |
| Architecture | Ampere | Blackwell |
| Form Factors | SXM4, PCIe | SXM, NVL |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink, PCIe 6.0, InfiniBand |
| Tensor Cores | 432 | 576 |
| FP16 Performance | 312 TFLOPS | 4,500 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 90 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 45 TFLOPS |
| INT8 Performance | 624 TOPS | 9,000 TOPS |
| Memory Bandwidth | 2,039 GB/s | 8,000 GB/s |
Performance Analysis
The B200 NVL demonstrates superior compute density compared to the A100 SXM4 80GB: its 4500 TFLOPS FP16 rate eclipses the A100's 312 TFLOPS by a factor of 14.4, accelerating deep learning training where half-precision dominates. FP32 performance follows suit at 90 TFLOPS versus 19.5 TFLOPS, a 4.6 times gain that benefits scientific simulations requiring single-precision arithmetic. The FP8 capability of 9000 TFLOPS on B200 further optimizes inference for quantized models, unavailable on A100.
Memory differences profoundly impact workloads: B200's 192 GB HBM3e VRAM and 8000 GB/s bandwidth dwarf A100's 80 GB HBM2e and 2039 GB/s, enabling larger batch sizes in LLM training and reducing data transfer bottlenecks. For instance, training billion-parameter models sees diminished I/O waits on B200, supporting effective batch sizes 3 to 4 times higher. Inference latency drops similarly due to sustained high throughput on massive datasets.
Power demands reflect these gains: B200's 1000W TDP doubles A100's 400W, necessitating robust cooling in SXM and NVL form factors with NVLink and PCIe 6.0 interconnects versus A100's PCIe 4.0.
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 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 | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) |
B200 NVL
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Nebius | NVIDIA B200 SXM 192GB VRAM | 192GB | 20 vCPU 224GB RAM | 🌍Europe | $3.95/GPU/hr | |||
 Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $4.79/GPU/hr $38.32/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.39/GPU/hr $43.12/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.69/GPU/hr $45.52/hr total (8×) | |||
 RunPod | NVIDIA B200 SXM 192GB VRAM | 192GB | 28 vCPU 283GB RAM | California | $5.89/GPU/hr |
When to Choose the A100 SXM4 80GB
The A100 SXM4 80GB suits budget-conscious deployments: cloud pricing starts at $0.13 per hour with an average of $1.28 per hour across 30 live offers, far below B200's $10.50 per hour. It handles fine-tuning, inference on models under 80 GB, and Stable Diffusion tasks efficiently with 312 TFLOPS FP16 and 2039 GB/s bandwidth.
Legacy infrastructure favors A100 due to PCIe 4.0 compatibility and widespread availability: teams avoid B200's single-offer scarcity and 1000W power requirements for moderate-scale AI workflows.
When to Choose the B200 NVL
The B200 NVL excels in cutting-edge AI training: 4500 TFLOPS FP16 and 192 GB VRAM manage trillion-parameter LLMs infeasible on A100's 80 GB limit. Its 8000 GB/s bandwidth sustains massive batches, slashing training times.
High-throughput inference demands B200: 9000 TFLOPS FP8 and PCIe 6.0 interconnects deliver sub-second latencies for enterprise-scale deployments, justifying $10.50 per hour for performance-critical applications.
Use Cases
B200's 4500 TFLOPS FP16 and 192 GB VRAM enable training of massive models far beyond A100's 312 TFLOPS and 80 GB capacity. Bandwidth of 8000 GB/s supports larger batches for faster convergence.
B200 leverages 9000 TFLOPS FP8 for ultra-low latency on large models, outperforming A100's FP16-only 312 TFLOPS. 192 GB VRAM accommodates full model loading without swapping.
A100's 80 GB VRAM and $1.28 per hour average suffice for models under 70 billion parameters. B200 accelerates with 4500 TFLOPS FP16 but at higher $10.50 per hour cost.
A100's 312 TFLOPS FP16 and 2039 GB/s bandwidth generate images efficiently at low $0.13 per hour starting price. B200's power overkill for typical diffusion model sizes.
A100's 19.5 TFLOPS FP32 matches many simulations at 400W TDP and broad availability. B200's 90 TFLOPS FP32 shines for extreme scales but demands 1000W infrastructure.
Frequently Asked Questions
What is the VRAM difference between A100 SXM4 80GB and B200 NVL?▾
B200 NVL provides 192 GB HBM3e VRAM, more than double the A100 SXM4 80GB's 80 GB HBM2e. This allows B200 to load larger models without partitioning. A100 suffices for workloads under 80 GB.
How do FP16 performance levels compare?▾
B200 NVL achieves 4500 TFLOPS FP16, 14.4 times higher than A100 SXM4 80GB's 312 TFLOPS. This translates to dramatically faster AI training on B200. Inference gains are similarly pronounced.
What are the current cloud prices?▾
A100 SXM4 80GB starts from $0.13 per hour, averaging $1.28 per hour across 30 offers. B200 NVL prices at $10.50 per hour across one offer. A100 offers better value currently.
Does B200 support FP8, and why does it matter?▾
B200 NVL delivers 9000 TFLOPS FP8, absent on A100. FP8 enables quantized inference with minimal accuracy loss, reducing latency for real-time serving. It suits high-volume deployments.
How does memory bandwidth differ?▾
B200 NVL's 8000 GB/s bandwidth quadruples A100 SXM4 80GB's 2039 GB/s. Higher bandwidth minimizes bottlenecks in large-batch training and data-heavy inference. Batch sizes can increase substantially on B200.
What are the TDP and form factor differences?▾
B200 NVL requires 1000W TDP in SXM or NVL forms, versus A100 SXM4 80GB's 400W in SXM4 or PCIe. B200 demands advanced cooling and power infrastructure. A100 fits broader existing setups.
Which is cheaper to rent, the A100 or the B200?▾
Cloud rental prices for both the A100 and B200 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 B200?▾
The A100 has 40 to 80 GB of HBM2e memory. The B200 has 192 GB of HBM3e memory.
Can I find A100 and B200 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 B200?▾
The A100 uses the Ampere architecture (2020) while the B200 uses Blackwell (2024). The B200 delivers 14.4x the FP16 throughput and 3.9x the memory bandwidth of the A100.