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 demonstrates overwhelming superiority in AI compute: its FP16 performance hits 1979 TFLOPS versus 65.3 TFLOPS on the RTX 5000 Ada, enabling dramatically faster model training where half-precision arithmetic dominates. FP32 rates are closer at 67 TFLOPS for H100 and 65.3 TFLOPS for RTX 5000 Ada, but the H100's FP8 capability of 3958 TFLOPS accelerates inference for quantized large language models, reducing latency in production deployments.
Memory bandwidth profoundly impacts real-world usage: 3350 GB/s on the H100 supports massive batch sizes in training runs, accommodating models exceeding 32 GB VRAM, while 576 GB/s on the RTX 5000 Ada limits scalability for datasets over moderate sizes. This disparity means H100 handles enterprise-scale deep learning without frequent swapping, whereas RTX 5000 Ada suits prototyping where smaller batches suffice.
Power demands further differentiate them: H100's 700W TDP suits dense server racks with NVLink interconnects, optimizing multi-GPU training, compared to RTX 5000 Ada's efficient 250W PCIe form factor for edge or single-node inference.
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 5000 Ada Generation
| 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 SXM5
The H100 SXM5 stands out for large-scale LLM training and inference requiring over 32 GB VRAM, such as models with billions of parameters: its 80 to 94 GB HBM3 and 3350 GB/s bandwidth enable batch sizes that RTX 5000 Ada cannot match. Cloud users facing tight deadlines benefit from 1979 TFLOPS FP16 and 3958 TFLOPS FP8, justifying $0.80 to $3.47 per hour pricing across 36 offers for high-throughput scientific computing or fine-tuning.
Multi-GPU clusters leverage H100's NVLink and SXM5 form factor for seamless scaling, unavailable on RTX 5000 Ada.
When to Choose the RTX 5000 Ada Generation
The RTX 5000 Ada Generation fits budget-conscious workflows with models under 32 GB VRAM: its 65.3 TFLOPS FP16 and FP32 performance handles Stable Diffusion or fine-tuning efficiently at $0.25 to $0.51 per hour across 5 offers. Lower 250W TDP and PCIe form factor simplify deployment in workstations or small cloud instances without advanced cooling.
Prototyping and inference for mid-sized AI tasks favor its Ada Lovelace efficiency over H100's datacenter focus.
Use Cases
H100's 80 to 94 GB HBM3 VRAM and 1979 TFLOPS FP16 support massive parameter counts and large batch sizes unattainable on RTX 5000 Ada's 32 GB GDDR6.
H100's 3958 TFLOPS FP8 performance enables quantized inference at high throughput, far exceeding RTX 5000 Ada's capabilities for production-scale deployments.
RTX 5000 Ada's 65.3 TFLOPS FP16 suffices for smaller models under 32 GB, while H100 accelerates larger ones with 1979 TFLOPS and superior bandwidth.
RTX 5000 Ada's 32 GB GDDR6 and 65.3 TFLOPS FP16 handle image generation efficiently at low $0.51 per hour average, without needing H100's excess capacity.
H100's 67 TFLOPS FP32, 3350 GB/s bandwidth, and NVLink interconnect optimize simulations and HPC workloads beyond RTX 5000 Ada's scope.
Frequently Asked Questions
What is the VRAM difference between H100 SXM5 and RTX 5000 Ada?▾
H100 SXM5 offers 80 to 94 GB HBM3 VRAM, enabling larger models than RTX 5000 Ada's 32 GB GDDR6. This gap affects batch sizes in training, with H100 supporting datasets RTX 5000 Ada cannot load fully. Memory bandwidth follows suit at 3350 GB/s versus 576 GB/s.
How do cloud prices compare for these GPUs?▾
H100 SXM5 starts at $0.80 per hour with an average of $3.47 per hour across 36 offers, while RTX 5000 Ada begins at $0.25 per hour averaging $0.51 per hour over 5 offers. Pricing reflects H100's datacenter performance versus RTX 5000 Ada's workstation efficiency. Users balance cost against 1979 TFLOPS FP16 on H100.
Which GPU is better for AI training?▾
H100 SXM5 excels with 1979 TFLOPS FP16 and 80 to 94 GB VRAM for large-scale training. RTX 5000 Ada's 65.3 TFLOPS limits it to smaller models. Bandwidth of 3350 GB/s on H100 further accelerates batches.
What are the power requirements?▾
H100 SXM5 demands 700W TDP, suited for server racks with NVLink. RTX 5000 Ada uses 250W, ideal for PCIe workstations. This influences cloud instance selection and cooling needs.
How does FP8 performance differ?▾
H100 provides 3958 TFLOPS FP8 for ultra-fast quantized inference, absent or minimal on RTX 5000 Ada. This boosts LLM serving efficiency on H100. FP16 also favors H100 at 1979 TFLOPS over 65.3 TFLOPS.
What architectures do they use?▾
H100 employs Hopper from 2022, optimized for AI with FP8 support. RTX 5000 Ada uses Ada Lovelace from 2023, graphics-focused. H100's specs yield higher throughput in compute tasks.
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.