Specifications Compared
| Spec | H100 | RTX-5000-ADA |
|---|---|---|
| TDP | 700W | 250W |
| VRAM | 80-94 GB | 32 GB |
| CUDA Cores | 16,896 | 12,800 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 400 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 65.3 TFLOPS |
| FP32 Performance | 67 TFLOPS | 65.3 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 1,044 TOPS |
| Memory Bandwidth | 3,350 GB/s | 576 GB/s |
Performance Analysis
The H100's FP16 throughput of 1979 TFLOPS vastly exceeds the RTX 5000 Ada's 65.3 TFLOPS, accelerating mixed-precision training where lower precision speeds up iterations without much accuracy loss. For inference, the H100's FP8 capability at 3958 TFLOPS enables ultra-fast serving of quantized large language models, a feature absent in the RTX 5000 Ada. FP32 performance remains close at 67 TFLOPS for the H100 and 65.3 TFLOPS for the RTX 5000 Ada, suiting general-purpose compute equally.
Memory differences prove critical: the H100's 80 to 94 GB HBM3 supports batch sizes for models exceeding 32 GB, preventing out-of-memory errors in training massive transformers. Its 3350 GB/s bandwidth sustains high data movement, allowing larger effective batch sizes than the RTX 5000 Ada's 576 GB/s and 32 GB limit, which constrains scalability in memory-bound tasks like fine-tuning.
Power draw highlights trade-offs: the H100's 700W TDP demands robust cooling and infrastructure, while the RTX 5000 Ada's 250W fits standard workstations. In real-world terms, the H100 processes workloads 30 times faster in FP16-heavy scenarios, but at six times the average hourly cost.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100
| 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×) |
RTX 5000 Ada
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA RTX 5000 Ada Generation 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Chubbuck, Idaho | $0.55/GPU/hr | Available | ||
 RunPod | NVIDIA RTX 5000 Ada Generation 32GB VRAM | 32GB | 10 vCPU 83GB RAM | 🌍global | $0.83/GPU/hr |
When to Choose the H100
The H100 excels in large-scale AI training and inference where models demand over 32 GB VRAM, such as billion-parameter LLMs fitting within its 80 to 94 GB HBM3. High memory bandwidth of 3350 GB/s supports massive batch sizes, reducing training time via its 1979 TFLOPS FP16 performance. Datacenter users leverage NVLink and InfiniBand interconnects for multi-GPU scaling across cloud instances starting at $0.80 per hour.
Enterprise environments prioritize the H100 for FP8 inference at 3958 TFLOPS, enabling real-time serving unattainable on the RTX 5000 Ada.
When to Choose the RTX 5000 Ada
The RTX 5000 Ada suits budget-limited workstations or small-scale ML with its 32 GB GDDR6 VRAM handling models up to that capacity at $0.25 per hour starting price. Lower 250W TDP integrates easily into desktops without specialized power setups, unlike the H100's 700W requirement. Balanced FP16 and FP32 at 65.3 TFLOPS each support prototyping and visualization tasks efficiently.
Solo developers or creative pros choose it for cost savings, averaging $0.51 per hour across fewer but accessible cloud offers.
Use Cases
The H100's 1979 TFLOPS FP16 and 80 to 94 GB HBM3 handle massive datasets and models infeasible on the RTX 5000 Ada's 32 GB VRAM. Its 3350 GB/s bandwidth supports large batch sizes for faster convergence.
FP8 performance at 3958 TFLOPS on the H100 delivers ultra-low latency for quantized models. High VRAM capacity serves larger batches than the RTX 5000 Ada's 65.3 TFLOPS FP16 limit.
H100's memory advantage of 80 to 94 GB over 32 GB prevents swapping during parameter-efficient fine-tuning of large LLMs. Superior bandwidth accelerates gradient updates.
RTX 5000 Ada's 32 GB VRAM and 65.3 TFLOPS FP16 suffice for image generation pipelines at lower cost of $0.25 per hour. Its PCIe form factor fits creative workflows without datacenter overhead.
H100's 67 TFLOPS FP32 and high interconnects like NVLink scale simulations across nodes. Vast bandwidth handles large datasets better than the RTX 5000 Ada's 576 GB/s.
Frequently Asked Questions
Which GPU has more VRAM: H100 or RTX 5000 Ada?▾
The H100 provides 80 to 94 GB HBM3 VRAM, surpassing the RTX 5000 Ada's 32 GB GDDR6. This allows the H100 to load larger AI models without partitioning. Memory bandwidth further differs at 3350 GB/s for H100 versus 576 GB/s.
How do H100 and RTX 5000 Ada compare in FP16 performance?▾
H100 achieves 1979 TFLOPS in FP16, over 30 times the RTX 5000 Ada's 65.3 TFLOPS. This gap accelerates deep learning training significantly. FP8 on H100 reaches 3958 TFLOPS, unavailable on the Ada GPU.
What is the power consumption of these GPUs?▾
The H100 has a 700W TDP, requiring datacenter cooling, while the RTX 5000 Ada uses 250W for workstation compatibility. Higher TDP correlates with H100's superior 1979 TFLOPS FP16. Efficiency varies by workload scale.
Which is cheaper in the cloud: H100 or RTX 5000 Ada?▾
RTX 5000 Ada starts at $0.25 per hour averaging $0.51 across 5 offers, versus H100's $0.80 minimum and $3.16 average over 58 offers. Cost reflects H100's datacenter capabilities. Availability favors H100 with more providers.
Can RTX 5000 Ada handle large LLM training like H100?▾
RTX 5000 Ada's 32 GB VRAM limits it to smaller models, unlike H100's 80 to 94 GB for billion-parameter LLMs. FP16 at 65.3 TFLOPS trails H100's 1979 TFLOPS, slowing training. Use RTX for prototyping only.
What architectures do H100 and RTX 5000 Ada use?▾
H100 employs Hopper from 2022 optimized for AI, while RTX 5000 Ada uses Ada Lovelace of 2023 for graphics and compute. H100 includes NVLink; RTX relies on PCIe. Specs show H100's focus on high-throughput AI.
Which is cheaper to rent, the H100 or the RTX 5000 Ada?▾
Cloud rental prices for both the H100 and RTX 5000 Ada 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 5000 Ada?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 5000 Ada has 32 GB of GDDR6 memory.
Can I find H100 and RTX 5000 Ada 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 5000 Ada?▾
The H100 uses the Hopper architecture (2022) while the RTX 5000 Ada uses Ada Lovelace (2023). The H100 delivers 30.3x the FP16 throughput and 5.8x the memory bandwidth of the RTX 5000 Ada.