Specifications Compared
| Spec | H100 | RTX-3080 |
|---|---|---|
| TDP | 700W | 320W |
| VRAM | 80-94 GB | 10-12 GB |
| CUDA Cores | 16,896 | 8,704 |
| Memory Type | HBM3 | GDDR6X |
| Architecture | Hopper | Ampere |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 272 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 29.8 TFLOPS |
| FP32 Performance | 67 TFLOPS | 29.8 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 3,350 GB/s | 760 GB/s |
Performance Analysis
Memory capacity defines a core disparity: the H100 PCE's 80 to 94 GB HBM3 VRAM supports massive models and large batch sizes, whereas the RTX 3080's 10 to 12 GB GDDR6X limits it to smaller datasets or requires model sharding. Bandwidth amplifies this: 3350 GB/s on the H100 PCIe enables rapid data throughput for training large language models, reducing bottlenecks, while 760 GB/s on the RTX 3080 constrains throughput for memory-intensive operations.
Compute throughput reveals training and inference implications. The H100 PCE's 1979 TFLOPS FP16 vastly outperforms the RTX 3080's 29.8 TFLOPS, accelerating mixed-precision training by over 66 times in theoretical peak. FP32 at 67 TFLOPS versus 29.8 TFLOPS benefits scientific simulations requiring single-precision math. FP8 capability at 3958 TFLOPS on the H100 PCIe optimizes low-precision inference for deployment-scale serving, unavailable on the RTX 3080. These deltas translate to hours versus days for model convergence in real-world deep learning pipelines.
Power and form factor influence deployment. The H100 PCE's 700W TDP suits enterprise cooling, paired with NVLink and PCIe 5.0 interconnects for multi-GPU scaling, while the RTX 3080's 320W and PCIe form factor fits consumer setups but lacks high-speed clustering.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100 PCIe
| 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 | ||
 Voltage Park | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 928GB RAM 19200GB Storage | Dallas, Texas | $1.99/GPU/hr $15.92/hr total (8×) |
When to Choose the H100 PCIe
The H100 PCIe excels in enterprise-scale AI training and inference where model sizes exceed 10 GB, such as large language models demanding 80 to 94 GB VRAM. Its 3350 GB/s bandwidth and 1979 TFLOPS FP16 enable batch sizes that minimize training time, justifying $1.25 to $2.75 per hour costs for production pipelines. Multi-GPU setups via NVLink make it ideal for distributed computing in research labs or cloud hyperscalers.
When to Choose the RTX 3080
The RTX 3080 suits budget-conscious prototyping and small-scale inference, handling models under 10 GB VRAM at $0.06 to $0.13 per hour. Its 29.8 TFLOPS FP16 and 760 GB/s bandwidth suffice for fine-tuning lightweight networks or Stable Diffusion tasks on modest datasets. Consumer PCIe compatibility lowers barriers for individual developers testing ideas before scaling.
Use Cases
LLM training requires massive VRAM and compute: H100 PCE's 80 to 94 GB HBM3 and 1979 TFLOPS FP16 handle billion-parameter models with large batches, far beyond RTX 3080's 10 to 12 GB capacity.
High-throughput inference benefits from FP8 at 3958 TFLOPS and 3350 GB/s bandwidth on H100 PCIe, enabling low-latency serving at scale. RTX 3080's 29.8 TFLOPS FP16 limits concurrent requests.
Fine-tuning large models demands high FP16 performance: H100 PCE's 1979 TFLOPS accelerates iterations, while RTX 3080's 10 to 12 GB VRAM restricts model sizes.
Stable Diffusion runs efficiently on 10 to 12 GB VRAM with 29.8 TFLOPS FP16 on RTX 3080, at low $0.13 per hour cost. H100 PCE overkill for single-image generation.
Scientific simulations leverage 67 TFLOPS FP32 and 3350 GB/s bandwidth on H100 PCIe for complex datasets. RTX 3080's 29.8 TFLOPS FP32 falls short for high-fidelity computations.
Frequently Asked Questions
Which GPU has more VRAM: H100 PCIe or RTX 3080?▾
The H100 PCIe offers 80 to 94 GB HBM3 VRAM, compared to 10 to 12 GB GDDR6X on the RTX 3080. This enables the H100 PCIe to load much larger AI models without offloading. RTX 3080 suits smaller workloads fitting within its limits.
How do FP16 performance numbers compare?▾
H100 PCIe delivers 1979 TFLOPS FP16, over 66 times the RTX 3080's 29.8 TFLOPS. This gap accelerates deep learning training significantly on H100 PCIe. Inference tasks also benefit from the higher throughput.
What is the price difference in cloud rentals?▾
H100 PCIe starts at $1.25 per hour with $2.75 average across 17 offers, versus RTX 3080 at $0.06 starting and $0.13 average across 4 offers. RTX 3080 provides extreme value for light use. H100 PCIe justifies cost for demanding jobs.
Does memory bandwidth matter for AI tasks?▾
H100 PCE's 3350 GB/s bandwidth supports large batch sizes in training, reducing I/O bottlenecks, against RTX 3080's 760 GB/s. Higher bandwidth cuts training time for memory-bound models. Lower bandwidth on RTX 3080 limits scalability.
What are the power requirements?▾
H100 PCIe consumes 700W TDP, requiring datacenter power infrastructure, while RTX 3080 uses 320W suitable for consumer PCs. This makes RTX 3080 easier for desktop setups. H100 PCIe demands enterprise cooling.
Can RTX 3080 handle large model training?▾
RTX 3080's 10 to 12 GB VRAM restricts it to models under that threshold, unlike H100 PCE's 80 to 94 GB. Techniques like quantization help but slow progress. H100 PCIe handles full-scale training natively.
Which is cheaper to rent, the H100 or the RTX 3080?▾
Cloud rental prices for both the H100 and RTX 3080 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 3080?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 3080 has 10 to 12 GB of GDDR6X memory.
Can I find H100 and RTX 3080 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 3080?▾
The H100 uses the Hopper architecture (2022) while the RTX 3080 uses Ampere (2020). The H100 delivers 66.4x the FP16 throughput and 4.4x the memory bandwidth of the RTX 3080.