NVIDIA / Ampere
RTX A5000
| Provider / plan | Price / GPU-hour | Rental type | Terms & configuration | Last checked | Provider pricing link |
|---|---|---|---|---|---|
| Vast.aiOn-demand offer 11925445 | $0.1889Lowest flexible rate | On-demandNo reservation | 1 GPU minimumAustralia, AUView 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 49579001 | $0.2289 | On-demandNo reservation | 1 GPU minimumLithuania, LTView 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 13273582 | $0.2329 | 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 ↗ |
| RunpodSecure Cloud Pods | $0.27 | 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 ↗ |
| Vast.aiOn-demand offer 50126512 | $0.5689 | On-demandNo reservation | 1 GPU minimumJapan, JPView 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 ↗ |
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 A5000 is a professional Ampere GPU with 24 GB of ECC memory. It suits visualization, rendering and AI development within a moderate memory budget. It supports an optional NVLink bridge for compatible two-GPU applications.
CHOOSING THIS GPU
Is it right for your workload?
RTX A5000 is an Ampere card. It is not the RTX 5000 Ada Generation or RTX PRO 5000 Blackwell.
Best suited to
- Professional graphics and rendering
- Small-model inference and AI development
- Supported two-card visualization or compute workloads
Strengths
- 24 GB ECC GDDR6
- 230 W reference board power
- Optional NVLink connectivity
Things to consider
- No native FP8 Tensor acceleration
- 24 GB memory budget
- NVLink and graphics licensing depend on the rental configuration
HARDWARE DETAILS
Inside the GPU
- Manufacturer
- NVIDIA
- Architecture
- Ampere
- VRAM (GB)
- 24
- Memory type
- GDDR6
- Form factor
- PCIe
- Memory bandwidth (GB/s)
- 768
- Maximum board power (W)
- 230
- CUDA cores
- 8192
- Tensor cores
- 256 / 3rd generation
- Ray tracing cores
- 64 / 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
Start with an 8B-class model in a supported 4-bit format. FP16 8B weights alone are about 16 GB before runtime memory; reserve room for KV cache and activations. Adapter fine-tuning needs a separate training memory estimate.
| 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 | 2Runtime needs more memory |
| Llama 3.1 70B ↗ | 8-bit | 70 GB | 3Runtime needs more memory |
| Llama 3.1 70B ↗ | FP16 | 140 GB | 6Runtime 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.