Specifications Compared
| Spec | A100 | GB300 |
|---|---|---|
| TDP | 400W | 1400W |
| VRAM | 40-80 GB | 288 GB |
| CUDA Cores | 6,912 | |
| Memory Type | HBM2e | HBM3e |
| Architecture | Ampere | Blackwell Ultra |
| Form Factors | SXM4, PCIe | SXM |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVSwitch, NVLink |
| Tensor Cores | 432 | |
| FP16 Performance | 312 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 90 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 45 TFLOPS |
| INT8 Performance | 624 TOPS | 4,500 TOPS |
| Memory Bandwidth | 2,039 GB/s | 12,000 GB/s |
Performance Analysis
Compute disparities define real-world advantages: the GB300 delivers 2250 TFLOPS in FP16 versus the A100's 312 TFLOPS, slashing training times for deep learning models, while 90 TFLOPS FP32 outperforms the A100's 19.5 TFLOPS for precision workloads like simulations. The GB300's FP8 capability at 4500 TFLOPS further accelerates inference on quantized large language models, enabling higher throughput than the A100's FP16 alone.
Memory specifications reshape scalability: 288 GB VRAM on the GB300 supports trillion-parameter models without extensive sharding, compared to the A100's 80 GB limit, and 12000 GB/s bandwidth versus 2039 GB/s permits larger batch sizes, reducing iterations in training cycles. The GB300's 1400W TDP demands advanced power infrastructure, contrasting the A100's efficient 400W draw. Interconnects enhance this: NVSwitch and NVLink on GB300 enable faster multi-GPU scaling over the A100's NVLink and PCIe 4.0.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 PCIe 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 | ||
 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×) |
When to Choose the A100 PCIe 80GB
The A100 PCIe 80GB excels in production environments requiring immediate availability: cloud rates from $0.89 per hour across 29 offers make it cost-effective for inference and fine-tuning with 312 TFLOPS FP16 and 80 GB VRAM. Its 400W TDP and mature Ampere ecosystem suit data centers avoiding high power upgrades, especially for models fitting within 2039 GB/s bandwidth constraints.
When to Choose the GB300 SXM6
The GB300 SXM6 targets cutting-edge AI research: 288 GB HBM3e VRAM and 12000 GB/s bandwidth handle massive datasets and large batch sizes infeasible on the A100. With 2250 TFLOPS FP16 and 4500 TFLOPS FP8, it accelerates training and inference for next-generation models once deployed.
Use Cases
GB300's 2250 TFLOPS FP16 and 288 GB VRAM accelerate training of massive models far beyond A100's 312 TFLOPS and 80 GB. Higher 12000 GB/s bandwidth supports larger batches.
GB300's 4500 TFLOPS FP8 optimizes quantized inference, with 288 GB VRAM fitting larger models than A100's 80 GB. It delivers over 7x FP16 performance at 2250 TFLOPS.
GB300 handles fine-tuning of large models with 90 TFLOPS FP32 and 12000 GB/s bandwidth, outperforming A100's 19.5 TFLOPS and 2039 GB/s.
A100's 312 TFLOPS FP16 suffices for current Stable Diffusion at $0.89 per hour. GB300's superior specs future-proof for advanced generative tasks.
A100's 19.5 TFLOPS FP32 and 400W TDP meet precision needs efficiently with proven availability. GB300's power demands suit only high-scale simulations.
Frequently Asked Questions
What is the VRAM capacity of NVIDIA A100 PCIe 80GB versus GB300 SXM6?▾
The A100 PCIe 80GB provides 80 GB HBM2e VRAM, while the GB300 SXM6 offers 288 GB HBM3e. This quadruples capacity for larger models on GB300. Bandwidth reaches 12000 GB/s on GB300 versus 2039 GB/s on A100.
How do FP16 performance figures compare between A100 and GB300?▾
GB300 achieves 2250 TFLOPS FP16, over seven times the A100's 312 TFLOPS. This boosts training and inference speeds significantly. GB300 adds 4500 TFLOPS FP8 for optimized workloads.
What are the cloud pricing details for these GPUs?▾
NVIDIA A100 PCIe 80GB starts at $0.89 per hour, averaging $2.06 per hour across 29 offers. GB300 SXM6 has no live offers as it launches in 2025. A100 provides immediate access.
What is the power consumption difference?▾
A100 PCIe 80GB has a 400W TDP, suitable for standard setups. GB300 SXM6 requires 1400W, needing advanced cooling. This impacts deployment costs.
Which GPU has better interconnects?▾
GB300 SXM6 uses NVSwitch and NVLink for superior multi-GPU scaling. A100 employs NVLink, PCIe 4.0, and InfiniBand. GB300 excels in large clusters.
When were these GPUs released?▾
A100 launched in 2020 on Ampere architecture. GB300 follows in 2025 on Blackwell Ultra. A100 offers mature support today.
Which is cheaper to rent, the A100 or the GB300?▾
Cloud rental prices for both the A100 and GB300 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 GB300?▾
The A100 has 40 to 80 GB of HBM2e memory. The GB300 has 288 GB of HBM3e memory.
Can I find A100 and GB300 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 GB300?▾
The A100 uses the Ampere architecture (2020) while the GB300 uses Blackwell Ultra (2025). The GB300 delivers 7.2x the FP16 throughput and 5.9x the memory bandwidth of the A100.