Specifications Compared
| Spec | H200 | QUADRO-RTX-6000 |
|---|---|---|
| TDP | 700W | 260W |
| VRAM | 141 GB | 24 GB |
| CUDA Cores | 16,896 | 4,608 |
| Memory Type | HBM3e | GDDR6 |
| Architecture | Hopper | Turing |
| Form Factors | SXM, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | NVLink |
| Tensor Cores | 528 | 576 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 67 TFLOPS | 16.3 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 4,800 GB/s | 672 GB/s |
Performance Analysis
The H200's FP16 performance of 1979 TFLOPS vastly outpaces the Quadro RTX 6000's 16.3 TFLOPS, accelerating deep learning training where half-precision computations dominate. For FP32 tasks common in scientific simulations, the H200's 67 TFLOPS remains over four times higher than the Quadro's 16.3 TFLOPS. This delta means training large neural networks completes in minutes on H200 versus hours on Quadro RTX 6000.
Memory capacity defines practical limits: 141 GB HBM3e on H200 supports batch sizes for models exceeding 100 billion parameters, while 24 GB GDDR6 on Quadro RTX 6000 restricts batches to small scales, often requiring gradient accumulation. Bandwidth reinforces this: 4800 GB/s on H200 minimizes data bottlenecks during inference, sustaining high throughput, whereas 672 GB/s on Quadro RTX 6000 causes stalls with memory-intensive workloads. FP8 capability at 3958 TFLOPS on H200 further optimizes quantized inference, unavailable on the older Turing GPU.
Power draw reflects efficiency gaps: H200's 700W TDP suits data centers, while Quadro RTX 6000's 260W fits workstations but yields lower absolute performance.
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 |
When to Choose the H200
Choose the H200 for large-scale AI training and inference where 141 GB HBM3e VRAM handles massive models without splitting. Its 1979 TFLOPS FP16 performance excels in cloud environments with pricing from $0.50 per hour. Scenarios include LLM development and scientific computing requiring 4800 GB/s bandwidth for optimal batch sizes.
Data center deployments benefit from SXM and NVL form factors with NVLink and PCIe 5.0 interconnects, unavailable on workstation-focused alternatives.
When to Choose the Quadro RTX 6000
Select the Quadro RTX 6000 for legacy workstation applications like CAD rendering or visualization where 24 GB GDDR6 suffices and 260W TDP minimizes power needs. It integrates via PCIe form factor without cloud dependency, ideal for on-premises setups lacking live cloud offers.
Budget-conscious users with existing Turing-compatible software prioritize its 16.3 TFLOPS FP32 for non-AI tasks over H200's data center orientation.
Use Cases
H200's 141 GB HBM3e VRAM and 1979 TFLOPS FP16 support massive parameter models without sharding. Quadro RTX 6000's 24 GB limits scale severely.
3958 TFLOPS FP8 and 4800 GB/s bandwidth on H200 enable high-throughput serving. Quadro RTX 6000's 672 GB/s bandwidth bottlenecks large batches.
67 TFLOPS FP32 and 141 GB VRAM on H200 accelerate adapter tuning for billion-parameter models. 24 GB on Quadro RTX 6000 requires inefficient techniques.
H200's superior FP16 at 1979 TFLOPS generates images faster with larger resolutions. Quadro RTX 6000 handles basic tasks but slower at 16.3 TFLOPS.
H200's 4800 GB/s bandwidth and 700W TDP optimize simulations with high data movement. Quadro RTX 6000 suits lighter workloads only.
Frequently Asked Questions
Which GPU has more VRAM: H200 or Quadro RTX 6000?▾
The H200 provides 141 GB HBM3e VRAM. Quadro RTX 6000 offers 24 GB GDDR6. This enables H200 to process much larger datasets.
How does H200 FP16 performance compare to Quadro RTX 6000?▾
H200 achieves 1979 TFLOPS in FP16. Quadro RTX 6000 delivers 16.3 TFLOPS. The gap accelerates AI training significantly on H200.
What is the memory bandwidth difference between H200 and Quadro RTX 6000?▾
H200 features 4800 GB/s bandwidth. Quadro RTX 6000 has 672 GB/s. Higher bandwidth on H200 supports larger batch sizes.
Is Quadro RTX 6000 available on cloud providers?▾
No live cloud offers exist for Quadro RTX 6000. H200 starts at $0.50 per hour across 26 offers averaging $3.62 per hour.
Which GPU uses less power: H200 or Quadro RTX 6000?▾
Quadro RTX 6000 has 260W TDP. H200 requires 700W. Lower TDP suits Quadro for workstations.
What architectures do H200 and Quadro RTX 6000 use?▾
H200 employs Hopper from 2024. Quadro RTX 6000 uses Turing from 2018. Newer Hopper drives superior compute.
Which is cheaper to rent, the H200 or the Quadro RTX 6000?▾
Cloud rental prices for both the H200 and Quadro RTX 6000 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 Quadro RTX 6000?▾
The H200 has 141 GB of HBM3e memory. The Quadro RTX 6000 has 24 GB of GDDR6 memory.
Can I find H200 and Quadro RTX 6000 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 Quadro RTX 6000?▾
The H200 uses the Hopper architecture (2024) while the Quadro RTX 6000 uses Turing (2018). The H200 delivers 121.4x the FP16 throughput and 7.1x the memory bandwidth of the Quadro RTX 6000.