Specifications Compared
| Spec | H200 | RTX-4080 |
|---|---|---|
| TDP | 700W | 320W |
| VRAM | 141 GB | 16 GB |
| CUDA Cores | 16,896 | 9,728 |
| Memory Type | HBM3e | GDDR6X |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 304 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 67 TFLOPS | 48.7 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 780 TOPS |
| Memory Bandwidth | 4,800 GB/s | 717 GB/s |
Performance Analysis
The H200 vastly outpaces the RTX 4080 in raw compute: FP16 at 1979 TFLOPS compared to 48.7 TFLOPS enables over 40 times faster tensor operations for AI training. FP32 performance shows a narrower gap at 67 TFLOPS versus 48.7 TFLOPS, but the H200 still leads for general compute. FP8 capability on the H200 reaches 3958 TFLOPS, ideal for quantized inference on large models.
Memory specifications define workload feasibility: 141 GB HBM3e on the H200 handles models exceeding 100 billion parameters with large batch sizes, while 16 GB GDDR6X on the RTX 4080 limits to smaller models or reduced batches. Bandwidth disparity is stark at 4800 GB/s versus 717 GB/s, reducing data bottlenecks on the H200 for memory-intensive tasks like LLM training.
Power draw impacts deployment: the H200's 700W TDP demands robust cooling and infrastructure, suiting datacenters, whereas the RTX 4080's 320W fits edge or multi-GPU consumer setups. These specs translate to the H200 accelerating large-scale inference by handling bigger contexts without swapping, while the RTX 4080 excels in latency-sensitive, lower-memory scenarios.
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 | 2×NVIDIA H200 SXM 141GB VRAM | 141GB | 48 vCPU 480GB RAM 6000GB Storage | London | $3.50/GPU/hr $7.00/hr total (2×) | Available |
RTX 4080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4080 SUPER 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA GeForce RTX 4080 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the H200
The H200 excels in enterprise AI training and large-scale inference. Its 141 GB VRAM accommodates full-precision LLMs up to hundreds of billions of parameters, and 4800 GB/s bandwidth supports batch sizes impossible on consumer GPUs. Teams scaling production workloads choose it for 1979 TFLOPS FP16 throughput across NVLink clusters.
When to Choose the RTX 4080
The RTX 4080 fits budget-conscious prototyping and small-to-medium inference. At $0.11 per hour starting price, it delivers 48.7 TFLOPS FP16 for fine-tuning models under 16 GB VRAM needs. Developers prioritize its 320W efficiency and PCIe compatibility for rapid experimentation or gaming-integrated compute.
Use Cases
The H200's 141 GB VRAM and 1979 TFLOPS FP16 handle large batch sizes for billion-parameter models. RTX 4080's 16 GB restricts scale.
3958 TFLOPS FP8 and 4800 GB/s bandwidth on H200 support high-throughput serving of massive LLMs. RTX 4080 suits only smaller models.
H200's 67 TFLOPS FP32 and vast memory enable efficient fine-tuning of large models. RTX 4080 works for datasets fitting 16 GB.
RTX 4080's 48.7 TFLOPS and lower $0.28 per hour cost optimize image generation pipelines. H200 overkill for typical diffusion models.
H200's 4800 GB/s bandwidth and NVLink accelerate simulations with large datasets. RTX 4080 adequate for modest HPC tasks.
Frequently Asked Questions
Which has more VRAM: H200 or RTX 4080?▾
The H200 provides 141 GB HBM3e VRAM. The RTX 4080 offers 16 GB GDDR6X. This gap allows H200 to load much larger AI models.
How do FP16 performances compare?▾
H200 achieves 1979 TFLOPS in FP16. RTX 4080 reaches 48.7 TFLOPS. H200 processes AI training over 40 times faster.
What are the cloud rental prices?▾
H200 starts at $0.50 per hour, averaging $3.62 across 26 offers. RTX 4080 begins at $0.11 per hour, averaging $0.28 over 8 offers.
Is H200 better for LLM inference?▾
Yes, H200's 3958 TFLOPS FP8 and 141 GB VRAM excel for large LLMs. RTX 4080 limits to smaller models due to 16 GB.
Power consumption difference?▾
H200 draws 700W TDP. RTX 4080 uses 320W. H200 requires datacenter power, RTX 4080 suits varied setups.
Memory bandwidth comparison?▾
H200 delivers 4800 GB/s. RTX 4080 provides 717 GB/s. Higher bandwidth on H200 minimizes stalls in data-heavy tasks.
Which is cheaper to rent, the H200 or the RTX 4080?▾
Cloud rental prices for both the H200 and RTX 4080 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 RTX 4080?▾
The H200 has 141 GB of HBM3e memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find H200 and RTX 4080 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 RTX 4080?▾
The H200 uses the Hopper architecture (2024) while the RTX 4080 uses Ada Lovelace (2022). The H200 delivers 40.6x the FP16 throughput and 6.7x the memory bandwidth of the RTX 4080.