NVIDIA / Blackwell
RTX PRO 6000 Blackwell
| Provider / plan | Price / GPU-hour | Rental type | Terms & configuration | Last checked | Provider pricing link |
|---|---|---|---|---|---|
| CloudRift3-month reservation | $1.10 | ReservedCommitment required | 1 GPU minimumSee provider3 months, upfrontView detailsPer-GPU published list rate; capacity and exact server configuration must be checked with the provider.
|
2026-10-03Auto-collected | View pricing ↗ |
| CloudRift1-month reservation | $1.16 | ReservedCommitment required | 1 GPU minimumSee provider1 month, upfrontView detailsPer-GPU published list rate; capacity and exact server configuration must be checked with the provider.
|
2026-10-03Auto-collected | View pricing ↗ |
| RunpodSecure Cloud Pods | $2.09Lowest flexible rate | On-demandNo reservation | 1 GPU minimumSee providerView detailsSecure Cloud Pods published GPU-hour rate. CPU/RAM allocation varies; storage/networking extra. Not live capacity.
|
2026-10-03Auto-collected | View pricing ↗ |
| ModalGPU Tasks | $3.0312GPU only · CPU/RAM extra | ServerlessActive GPU time | 1 GPU minimumSee providerView detailsGPU time only; CPU, RAM, storage, region and non-preemptible surcharges are extra.
|
2026-10-03Auto-collected | View pricing ↗ |
| CloudRiftOn-demand quote | Contact sales | On-demandNo reservation | 1 GPU minimumSee providerView detailsNo public on-demand price. Contact provider.
|
2026-09-28Manually checked | Request quote ↗ |
Prices are dated listings, not live availability. Compare minimum GPU counts and commitments. Storage, networking and taxes may add charges; serverless rates cover GPU time only.
RTX PRO 6000 Blackwell provides 96 GB of GDDR7 for AI and professional graphics workloads. Its memory capacity is useful for larger inference models and complex scenes. The catalog groups provider listings whose precise Server, Workstation or Max-Q edition must be confirmed.
CHOOSING THIS GPU
Is it right for your workload?
Edition is not normalized across providers. Shared memory specifications are shown; power, cooling and peak compute are edition-dependent. Server and Workstation reference pages are linked below.
Best suited to
- Large-memory inference on a professional RTX GPU
- Complex rendering and visualization scenes
- Mixed AI, graphics and video workloads
Strengths
- 96 GB GDDR7 with ECC
- Fifth-generation Tensor and fourth-generation RT technology
- PCIe Gen5 interface
Things to consider
- Confirm the exact edition before comparing peak compute or power
- Blackwell requires compatible drivers and software builds
- Multi-GPU topology is a property of the rental server
HARDWARE DETAILS
Inside the GPU
- Manufacturer
- NVIDIA
- Architecture
- Blackwell
- VRAM (GB)
- 96
- Memory type
- GDDR7
- Form factor
- Provider variant; confirm edition
- Memory bandwidth (GB/s)
- 1792
- Maximum board power (W)
- Not verified for this edition
- CUDA cores
- Not verified for this edition
- Tensor cores
- 5th generation
- Ray tracing cores
- 4th generation
- CUDA capability
- 12.0
- Memory ECC
- Supported
- Host interface
- PCIe Gen5
- GPU interconnect
- Not verified for this edition
- Hardware partitioning (MIG)
- Not verified for this edition
- Cooling
- Edition dependent; verify provider configuration
Hardware capabilities are not a guarantee of access in a cloud instance. Check the exact edition, GPU allocation and server topology in the rental offer.
MODEL MEMORY
Plan your model size
70B 8-bit weights alone are about 70 GB; a full runtime needs additional memory. Compare quantized single-GPU serving with supported multi-GPU execution. Full fine-tuning requires substantially more than inference weights.
| Example model class | Weight format | Raw weights ≈ | Minimum GPUs for weights only |
|---|---|---|---|
| Llama 3.1 8B ↗ | 4-bit | 4 GB | 1Runtime needs more memory |
| Llama 3.1 8B ↗ | FP16 | 16 GB | 1Runtime needs more memory |
| Qwen2.5 14B ↗ | 4-bit | 7 GB | 1Runtime needs more memory |
| Qwen2.5 32B ↗ | 4-bit | 16 GB | 1Runtime needs more memory |
| Llama 3.1 70B ↗ | 4-bit | 35 GB | 1Runtime needs more memory |
| Llama 3.1 70B ↗ | 8-bit | 70 GB | 1Runtime needs more memory |
| Llama 3.1 70B ↗ | FP16 | 140 GB | 2Runtime needs more memory |
Arithmetic lower bound: rounded parameter count × bits per weight ÷ 8, in decimal GB. Excludes quantization metadata, KV cache, activations, CUDA workspaces and training states. Actual model sizes differ from their rounded names. No context length, batch size or concurrency is guaranteed. GPU counts assume supported model sharding and can be higher in practice. Quantized checkpoints and compatible kernels are required for 4-bit/8-bit execution.
SOFTWARE & PERFORMANCE
Before you deploy
Software compatibility
Use an NVIDIA CUDA-enabled framework/container compatible with this GPU and the host driver. Check PyTorch build and inference-engine kernel requirements before deployment. vLLM documents NVIDIA compute capability 7.5+ as a baseline; support for each model and quantization method still needs checking. Select builds that explicitly support Blackwell; a container that runs on Hopper may not include the required kernels.
Measured benchmarks
A reproducible, comparable application benchmark has not yet been recorded for this edition. Peak TFLOPS above are not measured tokens per second or image-generation speed.
Compare tests using the same model, precision, GPU count, engine version, input/output length and batch or concurrency. A provider’s server configuration can change the result.
COMPARE YOUR OPTIONS
Alternatives to consider
BUDGET YOUR RUN
GPU cost estimator
GPU compute only: hourly rate × billable hours × GPU count. CPU, RAM, storage, egress, taxes, billing increments and minimum terms may add charges. Serverless hours mean active GPU task time; spot capacity can be interrupted. Reserved commitments and quote-only offers are excluded from this simple hourly estimator.