V100 SXM2 32GB vs RTX PRO 6000 Blackwell
NVIDIA V100 SXM2 32GB (Volta, 32 GB) against NVIDIA RTX PRO 6000 Blackwell (Blackwell, 96 GB): memory, compute, power and rental price, compared for LLM inference and training.
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Side-by-Side Specifications
| Spec | V100 SXM2 32GB | RTX PRO 6000 Blackwell |
|---|---|---|
| Architecture | Volta | Blackwell |
| Memory | 32 GB HBM2 | 96 GB GDDR7 |
| Memory bandwidth | 900 GB/s | 1,792 GB/s |
| FP16 tensor compute | 125 TFLOPS | 1,000 TFLOPS |
| INT8 tensor compute | 62.8 TOPS | 2,000 TOPS |
| Interconnect | NVLink 2.0 · 300 GB/s | PCIe 5.0 · 128 GB/s |
| TDP | 300 W | 600 W |
| Est. on-demand price | ~$2.00/h | ~$5.00/h |
| FP16 TFLOPS per $/h | 63 | 200 |
Highlighted values indicate the stronger spec. Hourly rates are indicative on-demand estimates.
Verdict
Raw performance: The RTX PRO 6000 Blackwell leads on FP16 tensor compute (8.0x advantage), which translates directly into higher token throughput for inference and shorter training steps.
Memory: With 96 GB per card, the RTX PRO 6000 Blackwell fits larger models on fewer GPUs — fewer cards means less inter-GPU communication and simpler deployments.
Value: At current on-demand rates, the RTX PRO 6000 Blackwell delivers more compute per dollar (200 vs 63 FP16 TFLOPS per $/h). If your model fits in its VRAM budget, it is usually the more economical choice.
GPUs Needed for Popular LLMs
Cards required to serve each model at 8-bit quantization (with 20% overhead for activations and KV cache).
| Model | VRAM (8-bit) | V100 SXM2 32GB | RTX PRO 6000 Blackwell |
|---|---|---|---|
| GPT-5.6 Sol | 2682 GB | 84x | 28x |
| DeepSeek V4 Pro (671B) | 750 GB | 24x | 8x |
| Muse Spark 1.1 | 335 GB | 11x | 4x |
| Claude 5 Sonnet (175B) | 196 GB | 7x | 3x |
| Nova Premier (80B) | 89 GB | 3x | 1x |
| Nova Core (34B) | 38 GB | 2x | 1x |
| Nova Lite (12B) | 13 GB | 1x | 1x |
| Phi 3.5 (3.8B) | 4 GB | 1x | 1x |
Frequently Asked Questions
Which is better for LLM inference: V100 SXM2 32GB or RTX PRO 6000 Blackwell?
The RTX PRO 6000 Blackwell delivers more raw FP16 compute (1,000 TFLOPS) and the RTX PRO 6000 Blackwell offers the most memory per card (96 GB). For cost-efficiency, the RTX PRO 6000 Blackwell currently gives more FP16 TFLOPS per dollar of on-demand rental (200 vs 63 TFLOPS per $/h).
How much more memory does the RTX PRO 6000 Blackwell have?
The V100 SXM2 32GB has 32 GB of HBM2 versus 96 GB of GDDR7 for the RTX PRO 6000 Blackwell — a ratio of 3.00x in favor of the RTX PRO 6000 Blackwell. More VRAM per card means fewer GPUs to fit a given model.
Is the V100 SXM2 32GB or the RTX PRO 6000 Blackwell cheaper to rent?
Estimated on-demand rates are ~$2.00/h for the V100 SXM2 32GB and ~$5.00/h for the RTX PRO 6000 Blackwell. Raw hourly price is only part of the story: normalize by throughput (TFLOPS per $/h) and by how many cards you need for your model's VRAM.
How do the V100 SXM2 32GB and RTX PRO 6000 Blackwell compare on power?
The V100 SXM2 32GB has a TDP of 300W versus 600W for the RTX PRO 6000 Blackwell. FP16 compute per watt: 0.4 vs 1.7 TFLOPS/W.
Deploy on a GPU cloud
Rent the V100 SXM2 32GB or RTX PRO 6000 Blackwell by the hour instead of buying hardware.
