NVIDIA / Ada Lovelace
RTX 6000 Ada
| Provider / plan | Price / GPU-hour | Rental type | Terms & configuration | Last checked | Provider pricing link |
|---|---|---|---|---|---|
| RunpodSecure Cloud Pods | $0.84Lowest 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 ↗ |
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 6000 Ada is a professional workstation GPU with 48 GB of ECC memory. It combines Ada compute, ray tracing and media engines for complex scenes and AI development. It is separate from the older RTX A6000 and newer RTX PRO 6000 Blackwell.
CHOOSING THIS GPU
Is it right for your workload?
RTX 6000 Ada Generation, 48 GB; not Quadro RTX 6000, RTX A6000 or RTX PRO 6000 Blackwell.
Best suited to
- Professional rendering and visualization
- AI development with a 48 GB memory budget
- Video and graphics workflows using AV1
Strengths
- 48 GB ECC memory
- 960 GB/s memory bandwidth
- Active workstation cooling and professional graphics features
Things to consider
- No NVLink
- 48 GB can require quantization for large models
- Verify graphics or virtual-workstation licensing in cloud offers
HARDWARE DETAILS
Inside the GPU
- Manufacturer
- NVIDIA
- Architecture
- Ada Lovelace
- VRAM (GB)
- 48
- Memory type
- GDDR6
- Form factor
- PCIe
- Memory bandwidth (GB/s)
- 960
- Maximum board power (W)
- 300
- CUDA cores
- 18176
- Tensor cores
- 568 / 4th generation
- Ray tracing cores
- 142 / 3rd generation
- CUDA capability
- 8.9
- Memory ECC
- Supported
- Host interface
- PCIe Gen4 x16
- GPU interconnect
- Not supported
- Hardware partitioning (MIG)
- Not verified for this edition
- Cooling
- Active; board design varies
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
Compare 8B/14B FP16 and larger quantized models using the weight-only estimates below. A 70B 4-bit model has about 35 GB of raw weights before quantization metadata and runtime memory; nominal capacity alone does not establish a fit.
| 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 | 2Runtime needs more memory |
| Llama 3.1 70B ↗ | FP16 | 140 GB | 3Runtime 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.
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.