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GPU comparison · Blackwell vs Hopper
NVIDIA RTX PRO 6000 vs H100.
Two ways to get 80–96 GB per GPU. The RTX PRO 6000 (Blackwell, PCIe) has more memory, FP4 and RT cores at about half the price; the H100 SXM (Hopper) has twice the memory bandwidth and NVLink between GPUs, which matters for multi-GPU training.
- 96 vs 80 GBGPU memory
- 1.6 vs 3.35 TB/smemory bandwidth
- $959 vs $1,779per GPU, per month
RTX PRO 6000 vs H100: which one to rent.
Choose the RTX PRO 6000 if
- You serve or fine-tune on single GPUs: 96 GB holds a 70B model in FP8 on one card.
- You want FP4 inference, rendering or video engines on the same card.
- Price matters: $959 per GPU against $1,779 a month.
Choose the H100 if
- You train across several GPUs: NVLink 4 moves 900 GB/s per GPU; the RTX PRO 6000 talks over PCIe.
- Memory bandwidth drives your speed: 3.35 TB/s against 1.6 TB/s.
- You want the most mature LLM software stack, tuned for Hopper.
RTX PRO 6000 vs H100 specs, side by side.
Figures from NVIDIA’s published specifications, per GPU. Tensor figures are peak dense throughput.
| Per GPU | NVIDIA RTX PRO 6000 | NVIDIA H100 | H100 / RTX PRO 6000 |
|---|---|---|---|
| Architecture | Blackwell | Hopper, TSMC 4N | — |
| Form factor | Server Edition, passive PCIe card | SXM5 module | — |
| GPU memory | 96 GB GDDR7 with ECC, 512-bit | 80 GB HBM3, 5,120-bit | 0.83× |
| Memory bandwidth | 1,597 GB/s | 3.35 TB/s | 2.1× |
| GPU-to-GPU link | None (PCIe 5.0 x16) | NVLink 4, 900 GB/s per GPU | — |
| FP4 tensor (dense) | 4.0 PFLOPS | Not supported | — |
| FP8 tensor (dense) | 2,000 TFLOPS | 1,979 TFLOPS | Same |
| FP16 / BF16 tensor (dense) | 1,000 TFLOPS | 989 TFLOPS | Same |
| FP64 | — | 34 / 67 TFLOPS | — |
| Max power | Up to 600 W, configurable | Up to 700 W, configurable | — |
| GPUs per server | 1, 2, 4, 8 | 1, 2, 4, 8 | — |
| Per GPU in our servers | 24 vCPU · 180 GB RAM · 1.92 TB | 20 vCPU · 200 GB RAM · 2 TB | — |
NVIDIA does not say whether its RTX PRO 6000 Server Edition tensor figures use sparsity. Sources: NVIDIA RTX PRO 6000 Server Edition · NVIDIA H100. Full sheets: RTX PRO 6000 · H100
RTX PRO 6000 vs H100 rental price.
Our monthly prices, set 30% below the market median of public on-demand prices and paid in crypto. Hourly figures are the monthly price divided by 730 hours.
| CryptGPU | NVIDIA RTX PRO 6000 | NVIDIA H100 | H100 / RTX PRO 6000 |
|---|---|---|---|
| Price per GPU, per month | $959 | $1,779 | 1.86× |
| Equivalent per GPU-hour | $1.31 | $2.44 | — |
| Per GB of GPU memory, per month | $9.99 | $22.24 | 2.2× |
| Per PFLOPS of dense FP8, per month | $480 | $899 | 1.87× |
| Largest server | 8× · $7,672/mo | 8× · $14,232/mo | — |
| Market median, per GPU | $1,380 | $2,548 | — |
| Below the median | −31% | −30% | — |
Same price per GPU at every server size, no hourly metering. How we compare prices · Configure an RTX PRO 6000 server · Configure a H100 server
Which models fit on each.
Smallest server that holds each model, by precision. Rule of thumb with headroom for the KV cache, 92% of GPU memory usable.
| Model (total parameters) | RTX PRO 6000 · FP8 | H100 · FP8 | RTX PRO 6000 · 4-bit | H100 · 4-bit |
|---|---|---|---|---|
| Qwen3.5-9B | 1× | 1× | 1× | 1× |
| Gemma 4 31B | 1× | 1× | 1× | 1× |
| Llama 3.3 70B | 1× | 2× | 1× | 1× |
| gpt-oss-120b | 2× | 2× | 1× | 2× |
| DeepSeek-V4-Flash | 4× | 8× | 4× | 4× |
| Qwen3.5-397B-A17B | 8× | 8× | 4× | 4× |
| DeepSeek-R1 (671B) | — | — | 8× | 8× |
| Kimi K2.6 (1T) | — | — | 8× | — |
FP8 ≈ 1.2 bytes and 4-bit ≈ 0.65 bytes per parameter. — = larger than the biggest server of that GPU. Size another model · How much VRAM does an LLM need?
RTX PRO 6000 vs H100 FAQ.
More on each GPU: NVIDIA RTX PRO 6000 · NVIDIA H100.
Is the RTX PRO 6000 an H100 alternative for inference?
Often, yes: it has more memory (96 GB against 80 GB) and FP4 for about half the price. The H100 generates tokens faster per GPU where bandwidth is the limit, and scales better across GPUs.
Which is better for training?
The H100 for multi-GPU training, thanks to NVLink and HBM bandwidth. The RTX PRO 6000 suits single-GPU fine-tuning (LoRA, QLoRA) of large models.
Are the tensor figures comparable?
Not exactly: NVIDIA does not say whether its RTX PRO 6000 Server Edition figures use sparsity, while the H100 figures we show are dense. Compare memory and bandwidth first.
Other GPU comparisons.
Rent the RTX PRO 6000 or the H100.
Dedicated servers with 1 to 8 GPUs, one monthly price, paid in crypto. Online in under 10 minutes, no KYC.