Specifications Compared
| Spec | H200 | P100 |
|---|---|---|
| TDP | 700W | 250W |
| VRAM | 141 GB | 16 GB |
| CUDA Cores | 16,896 | 3,584 |
| Memory Type | HBM3e | HBM2 |
| Architecture | Hopper | Pascal |
| Form Factors | SXM, NVL | SXM2, PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | NVLink |
| Tensor Cores | 528 | |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 9.3 TFLOPS |
| FP32 Performance | 67 TFLOPS | 9.3 TFLOPS |
| FP64 Performance | 34 TFLOPS | 4.7 TFLOPS |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 4,800 GB/s | 732 GB/s |
Performance Analysis
The H200's FP16 throughput of 1979 TFLOPS vastly outpaces the P100's 9.3 TFLOPS, enabling over 212 times faster matrix operations critical for deep learning training. FP32 performance shows 67 TFLOPS against 9.3 TFLOPS, a sevenfold gain suited for scientific simulations. This delta accelerates training epochs: large neural networks process batches quicker on H200, reducing time from days to hours.
Memory specs define real-world limits. H200's 141 GB HBM3e versus P100's 16 GB HBM2 supports massive models without splitting, while 4800 GB/s bandwidth versus 732 GB/s permits larger batch sizes in inference, minimizing latency. For example, transformer models exceeding 16 GB fail on P100 but thrive on H200, enhancing throughput in production.
Power draw underscores efficiency gaps: H200 at 700W TDP delivers disproportionate compute per watt compared to P100's 250W, vital for dense cloud deployments.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H200
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vultr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 960GB Storage | Atlanta | $1.99/GPU/hr | Available | ||
 Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | Virginia | $2.29/GPU/hr | Available | ||
 Nebius | NVIDIA H200 SXM 141GB VRAM | 141GB | 16 vCPU 200GB RAM | 🌍Europe | $2.45/GPU/hr | |||
 CoreWeave | 8×NVIDIA H200 SXM 141GB VRAM | 141GB | 128 vCPU 0GB RAM 61440GB Storage | United States | $2.58/GPU/hr $20.64/hr total (8×) | |||
 Ori | NVIDIA H200 SXM 141GB VRAM | 141GB | 24 vCPU 240GB RAM 3000GB Storage | London | $3.50/GPU/hr | Available |
P100
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 2×NVIDIA Tesla P100 16GB VRAM | 16GB | 0 vCPU 256GB RAM 960GB Storage | Netherlands | $0.60/GPU/hr $1.20/hr total (2×) | Available |
When to Choose the H200
Select the H200 for large-scale AI workloads demanding high VRAM and throughput. Its 141 GB HBM3e handles LLMs over 100 billion parameters intact, unlike P100's 16 GB limit. FP8 performance at 3958 TFLOPS excels in inference for real-time applications, with NVLink and PCIe 5.0 ensuring scalable multi-GPU clusters.
When to Choose the P100
Opt for the P100 in budget-constrained or legacy environments. At $0.07 per hour from $0.25 average, it suits small-scale prototyping or Pascal-optimized codebases. Its 250W TDP fits low-power setups, and 9.3 TFLOPS FP16 suffices for basic CNN training without modern memory needs.
Use Cases
H200's 1979 TFLOPS FP16 and 141 GB VRAM support massive datasets and models, while P100's 9.3 TFLOPS and 16 GB cause out-of-memory errors.
FP8 at 3958 TFLOPS and 4800 GB/s bandwidth on H200 enable low-latency serving of large models; P100 lacks capacity for models over 16 GB.
67 TFLOPS FP32 and high bandwidth allow efficient parameter updates on H200; P100 struggles with batch sizes beyond 732 GB/s limits.
H200's VRAM holds full diffusion models for high-resolution generation; P100's 16 GB restricts to low-res or tiled outputs.
P100's 9.3 TFLOPS FP32 matches many simulations at $0.07 per hour; H200 overkill unless memory exceeds 16 GB.
Frequently Asked Questions
How much faster is H200 than P100 in FP16?▾
H200 achieves 1979 TFLOPS FP16 versus P100's 9.3 TFLOPS, over 212 times higher. This translates to drastically shorter training times for deep learning. Bandwidth at 4800 GB/s further amplifies gains over 732 GB/s.
What is the VRAM difference between H200 and P100?▾
H200 offers 141 GB HBM3e, nearly nine times P100's 16 GB HBM2. This enables larger models on H200 without sharding. P100 suits only sub-16 GB workloads.
Is P100 still viable for cloud use?▾
P100 provides value at $0.07 per hour average $0.25, ideal for legacy Pascal apps. Its 250W TDP aids power-sensitive setups. Modern tasks demand H200's specs.
H200 pricing compared to P100?▾
H200 starts at $0.50 per hour averaging $3.62 across 26 offers; P100 at $0.07 averaging $0.25 across 3. Cost reflects 2024 Hopper versus 2016 Pascal performance.
Can P100 handle LLM inference?▾
P100's 16 GB VRAM limits it to small LLMs under that threshold at 9.3 TFLOPS FP16. H200's 141 GB and 3958 TFLOPS FP8 serve production-scale models.
Power consumption of H200 vs P100?▾
H200 draws 700W TDP, P100 250W. H200's higher draw yields superior compute density for AI clusters. P100 fits edge or low-power clouds.
Which is cheaper to rent, the H200 or the P100?▾
Cloud rental prices for both the H200 and P100 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 H200 have compared to the P100?▾
The H200 has 141 GB of HBM3e memory. The P100 has 16 GB of HBM2 memory.
Can I find H200 and P100 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 H200 and the P100?▾
The H200 uses the Hopper architecture (2024) while the P100 uses Pascal (2016). The H200 delivers 212.8x the FP16 throughput and 6.6x the memory bandwidth of the P100.