NVIDIA / Ampere
RTX A6000
| Provider / plan | Price / GPU-hour | Rental type | Terms & configuration | Last checked | Provider pricing link |
|---|---|---|---|---|---|
| Vast.aiOn-demand offer 21004761 | $0.4022Lowest flexible rate | On-demandNo reservation | 1 GPU minimumKansas, USView detailsObserved marketplace offer, not a provider-wide list rate. Total hourly quote for one GPU with the API default disk allocation; bandwidth and additional storage can cost extra. Up to 5 cheapest matching offers per catalog GPU; availability can change.
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| Vast.aiOn-demand offer 53677118 | $0.4030 | On-demandNo reservation | 1 GPU minimumIndia, INView detailsObserved marketplace offer, not a provider-wide list rate. Total hourly quote for one GPU with the API default disk allocation; bandwidth and additional storage can cost extra. Up to 5 cheapest matching offers per catalog GPU; availability can change.
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2026-10-03Auto-collected | View pricing ↗ |
| Vast.aiOn-demand offer 51222101 | $0.4037 | On-demandNo reservation | 1 GPU minimumRomania, ROView detailsObserved marketplace offer, not a provider-wide list rate. Total hourly quote for one GPU with the API default disk allocation; bandwidth and additional storage can cost extra. Up to 5 cheapest matching offers per catalog GPU; availability can change.
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2026-10-03Auto-collected | View pricing ↗ |
| Vast.aiOn-demand offer 51222180 | $0.4037 | On-demandNo reservation | 1 GPU minimumRomania, ROView detailsObserved marketplace offer, not a provider-wide list rate. Total hourly quote for one GPU with the API default disk allocation; bandwidth and additional storage can cost extra. Up to 5 cheapest matching offers per catalog GPU; availability can change.
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2026-10-03Auto-collected | View pricing ↗ |
| Vast.aiOn-demand offer 53398439 | $0.4044 | On-demandNo reservation | 1 GPU minimumArizona, USView detailsObserved marketplace offer, not a provider-wide list rate. Total hourly quote for one GPU with the API default disk allocation; bandwidth and additional storage can cost extra. Up to 5 cheapest matching offers per catalog GPU; availability can change.
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2026-10-03Auto-collected | View pricing ↗ |
| RunpodSecure Cloud Pods | $0.53 | On-demandNo reservation | 1 GPU minimumSee providerView detailsSecure Cloud Pods published GPU-hour rate. CPU/RAM allocation varies; storage/networking extra. Not live capacity.
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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 A6000 is an Ampere workstation GPU with 48 GB of ECC GDDR6. It is useful for larger scenes, AI development and model workloads that exceed a 24 GB card. An optional NVLink bridge connects two cards when the application supports distributed memory.
CHOOSING THIS GPU
Is it right for your workload?
RTX A6000 is the Ampere model, not RTX 6000 Ada. A linked pair has 96 GB total, but applications must explicitly support multi-GPU memory use.
Best suited to
- Large-scene rendering
- 48 GB-class inference and fine-tuning experiments
- Supported two-GPU workflows with an NVLink bridge
Strengths
- 48 GB ECC memory
- Optional NVLink
- Professional graphics and active cooling
Things to consider
- No native FP8 Tensor acceleration
- NVLink bridge may be absent on rented instances
- Memory pooling is application-dependent
HARDWARE DETAILS
Inside the GPU
- Manufacturer
- NVIDIA
- Architecture
- Ampere
- VRAM (GB)
- 48
- Memory type
- GDDR6
- Form factor
- PCIe
- Memory bandwidth (GB/s)
- 768
- Maximum board power (W)
- 300
- CUDA cores
- 10752
- Tensor cores
- 336 / 3rd generation
- Ray tracing cores
- 84 / 2nd generation
- CUDA capability
- 8.6
- Memory ECC
- Supported
- Host interface
- PCIe Gen4 x16
- GPU interconnect
- Two GPUs; 112.5 GB/s aggregate bidirectional
- 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.