Specifications Compared
| Spec | H100 | RTX-A2000 |
|---|---|---|
| TDP | 700W | 70W |
| VRAM | 80-94 GB | 6-12 GB |
| CUDA Cores | 16,896 | 3,328 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Ampere |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 104 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 8 TFLOPS |
| FP32 Performance | 67 TFLOPS | 8 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 3,350 GB/s | 288 GB/s |
Performance Analysis
Compute disparities define real-world applicability: the H100 SXM5 achieves 1979 TFLOPS in FP16 versus the A2000's 8 TFLOPS, accelerating deep learning training by orders of magnitude. FP32 performance follows suit at 67 TFLOPS for H100 compared to 8 TFLOPS for A2000, benefiting scientific simulations and rendering. The H100's FP8 capability at 3958 TFLOPS further optimizes large-scale inference.
Memory bandwidth profoundly impacts workloads: H100's 3350 GB/s supports batch sizes for models exceeding 100 billion parameters, while A2000's 288 GB/s limits it to smaller batches around 1 to 10 million parameters. This enables H100 for enterprise training runs but restricts A2000 to prototyping.
Power consumption underscores deployment differences: H100's 700W TDP suits data centers, whereas A2000's 70W fits edge or multi-GPU setups. Overall, H100 excels in high-throughput AI, A2000 in efficient, low-demand tasks.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100 SXM5
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Hyperstack | 4×NVIDIA H100 PCIe 80GB VRAM | 80GB | 124 vCPU 720GB RAM 3300GB Storage | Canada | $1.90/GPU/hr $7.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA H100 PCIe 80GB VRAM | 80GB | 60 vCPU 360GB RAM 1600GB Storage | Canada | $1.90/GPU/hr $3.80/hr total (2×) | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.90/GPU/hr $15.20/hr total (8×) | Available | ||
Hyperstack | NVIDIA H100 PCIe 80GB VRAM | 80GB | 28 vCPU 180GB RAM 850GB Storage | Canada | $1.90/GPU/hr | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.95/GPU/hr $15.60/hr total (8×) | Available |
RTX A2000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA RTX A2000 12GB VRAM | 12GB | 6 vCPU 20GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the H100 SXM5
Opt for the H100 SXM5 in large-scale AI training and inference: its 80 to 94 GB HBM3 VRAM handles models like GPT-scale LLMs, and 3350 GB/s bandwidth supports massive batches. Datacenter environments leverage NVLink and PCIe 5.0 for multi-GPU scaling at 1979 TFLOPS FP16.
Scientific computing benefits from 67 TFLOPS FP32, far surpassing A2000 capabilities.
When to Choose the RTX A2000
Choose the RTX A2000 for cost-sensitive development: at $0.06 per hour minimum, it runs small model inference or fine-tuning with 6 to 12 GB GDDR6. Its 70W TDP enables dense deployments without high power infrastructure.
Prototyping Stable Diffusion or lightweight tasks fits perfectly, given 8 TFLOPS FP16/FP32 performance.
Use Cases
H100 SXM5's 80 to 94 GB HBM3 and 1979 TFLOPS FP16 handle massive datasets and parameters. A2000's 6 to 12 GB VRAM cannot support large LLMs.
H100's 3958 TFLOPS FP8 and high bandwidth enable high-throughput serving. A2000 suits only tiny models due to memory constraints.
H100's 67 TFLOPS FP32 and vast VRAM accelerate parameter-efficient tuning on big models. A2000 limits scale with 8 TFLOPS.
A2000's 8 TFLOPS FP16 suffices for image generation at 6 to 12 GB VRAM. H100 overkill for single-user workflows.
H100's 67 TFLOPS FP32 and 3350 GB/s bandwidth excel in simulations. A2000's 8 TFLOPS restricts complex computations.
Frequently Asked Questions
What is the VRAM difference between H100 SXM5 and RTX A2000?▾
H100 SXM5 offers 80 to 94 GB HBM3 VRAM, enabling large models. RTX A2000 provides 6 to 12 GB GDDR6, suitable for smaller tasks. This gap affects batch sizes and model capacity.
How do FP16 performances compare?▾
H100 SXM5 delivers 1979 TFLOPS FP16 for rapid training. RTX A2000 achieves 8 TFLOPS, adequate for basic inference. H100 suits high-scale AI workloads.
What are the cloud pricing differences?▾
H100 SXM5 starts at $0.80 per hour, averaging $3.44 across 37 offers. RTX A2000 begins at $0.06 per hour, averaging $0.23 across 3 offers. A2000 favors budget use.
Which has higher memory bandwidth?▾
H100 SXM5 provides 3350 GB/s, supporting huge batches. RTX A2000 offers 288 GB/s for lighter loads. Bandwidth dictates data throughput.
What are the TDP ratings?▾
H100 SXM5 consumes 700W for datacenter power. RTX A2000 uses 70W, ideal for efficient setups. This impacts cooling and density.
Is H100 better for LLM training?▾
Yes, H100's 1979 TFLOPS FP16 and 80 to 94 GB VRAM excel in LLM training. A2000's specs limit it to small models only.
Which is cheaper to rent, the H100 or the RTX A2000?▾
Cloud rental prices for both the H100 and RTX A2000 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 H100 have compared to the RTX A2000?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX A2000 has 6 to 12 GB of GDDR6 memory.
Can I find H100 and RTX A2000 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 H100 and the RTX A2000?▾
The H100 uses the Hopper architecture (2022) while the RTX A2000 uses Ampere (2021). The H100 delivers 247.4x the FP16 throughput and 11.6x the memory bandwidth of the RTX A2000.