RTX Pro 6000 vs H100
Explore a head to head comparison of specifications, performance, and pricing.
RTX Pro 6000
The NVIDIA RTX Pro 6000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
H100
The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.
RTX Pro 6000 vs H100: Which Should You Choose?
The RTX Pro 6000 offers 96 GB of VRAM — 1.2× the 80 GB on the H100 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H100 delivers 267.6 TFLOPS versus 126 TFLOPS on the RTX Pro 6000 — 2× faster for mixed-precision training and inference. Memory bandwidth favors the H100 at 0.00 TB/s compared to 0.00 TB/s on the RTX Pro 6000, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the RTX Pro 6000 is built on Blackwell while the H100 uses Hopper, reflecting different generational capabilities and optimizations. On Shadeform, the RTX Pro 6000 starts from $1.25/hr versus $1.66/hr for the H100 — 33% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 7 for the RTX Pro 6000, giving more options for region and pricing flexibility.
RTX Pro 6000 — Best Use Cases
- •Next-generation LLM pre-training at scale
- •Trillion-parameter model inference
- •Ultra-high-throughput AI workloads
- •Advanced HPC and scientific computing
Choose RTX Pro 6000 when:
- ✓You need 96 GB+ VRAM for large models or long context windows
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
H100 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H100 when:
- ✓80 GB VRAM is sufficient for your workload
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
See how the RTX Pro 6000 & H100 compare
Compare detailed hardware specifications and average pricing for the RTX Pro 6000 and H100.
Compare Hardware Specifications
| RTX Pro 6000 | H100 | |
|---|---|---|
| GPU Type | RTX Pro 6000 | H100 |
| VRAM per GPU | 96 GB | 80 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Blackwell | Hopper |
| Interconnect | PCIe Gen5 | PCIe Gen5 or SXM5 |
| Memory Bandwidth | 1.59 TB/s | 3.35 TB/s |
| FP16 TFLOPS | 126.0 TFLOPS (1:1) | 267.6 TFLOPS (4:1) |
| CUDA Cores | 24064 | 16896 |
| Tensor Cores | 752 (5th Gen) | 528 (4th Gen) |
| RT Cores | 188 (4th Gen) | N/A |
| Base Clock | 1860 MHz | 1365 MHz |
| Boost Clock | 2600 MHz | 1785 MHz |
| TDP | 400W | 350-700W |
| Process Node | TSMC 4N | TSMC 4N |
| Data Formats | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32 | FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| RTX Pro 6000 | H100 | |
|---|---|---|
| 1 GPU | $1.70 /hr | $2.85 /hr |
| 2 GPUs | $3.30 /hr | $5.19 /hr |
| 4 GPUs | $6.60 /hr | $9.79 /hr |
| 8 GPUs | $14.11 /hr | $19.35 /hr |
Frequently Asked Questions: RTX Pro 6000 vs H100
The main differences are VRAM (96 GB vs 80 GB), FP16 throughput (126 vs 267.6 TFLOPS), architecture (Blackwell vs Hopper). The RTX Pro 6000 uses the Blackwell architecture while the H100 is based on Hopper, giving each GPU different generational capabilities.
The H100 is generally better for large language model training due to its higher throughput and 80 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the RTX Pro 6000 is available from $1.25/hr. The H100 starts from $1.66/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The RTX Pro 6000 has more VRAM at 96 GB, compared to 80 GB on the H100. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the H100 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the RTX Pro 6000, paying the premium may be justified by faster job completion and lower total cost.
The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 7 for the RTX Pro 6000. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either RTX Pro 6000 or H100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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