NVIDIA / Ampere
A100 SXM 80GB
| Provider / plan | Price / GPU-hour | Rental type | Terms & configuration | Last checked | Provider pricing link |
|---|---|---|---|---|---|
| CloudRift3-month reservation | $0.89 | ReservedCommitment required | 1 GPU minimumSee provider3 months, upfrontView detailsPer-GPU published list rate; capacity and exact server configuration must be checked with the provider.
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| CloudRift1-month reservation | $0.95 | ReservedCommitment required | 1 GPU minimumSee provider1 month, upfrontView detailsPer-GPU published list rate; capacity and exact server configuration must be checked with the provider.
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2026-10-03Auto-collected | View pricing ↗ |
| Vast.aiOn-demand offer 47239986 | $1.0124Lowest flexible rate | On-demandNo reservation | 1 GPU minimumTexas, 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 ↗ |
| CloudRiftOn-demand | $1.05 | On-demandNo reservation | 1 GPU minimumSee providernoneView detailsPer-GPU published list rate; capacity and exact server configuration must be checked with the provider.
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2026-10-03Auto-collected | View pricing ↗ |
| Vast.aiOn-demand offer 21050987 | $1.0561 | On-demandNo reservation | 1 GPU minimumSweden, SEView 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 32744972 | $1.1627 | On-demandNo reservation | 1 GPU minimumCzechia, CZView 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 34475474 | $1.1893 | On-demandNo reservation | 1 GPU minimumCzechia, CZView 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 53572338 | $1.5028 | On-demandNo reservation | 1 GPU minimumNorth Carolina, 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 | $1.59 | 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.
A100 SXM 80GB is an Ampere data-center accelerator for training, fine-tuning and inference. Its 80 GB HBM2e memory and NVLink connectivity make it useful for memory-heavy jobs and multi-GPU deployments. It predates Hopper FP8 acceleration.
CHOOSING THIS GPU
Is it right for your workload?
80 GB SXM edition; the 40 GB A100 and 80 GB PCIe models have different memory or power specifications.
Best suited to
- BF16 and FP16 training or fine-tuning
- Inference for models requiring an 80 GB memory class
- Multi-GPU workloads on a connected SXM server
Strengths
- 80 GB HBM2e
- 600 GB/s NVLink hardware capability
- MIG hardware partitioning support
Things to consider
- No native FP8 Tensor Core acceleration
- 400 W reference board power
- Check that a rental is a full GPU rather than a MIG slice
HARDWARE DETAILS
Inside the GPU
- Manufacturer
- NVIDIA
- Architecture
- Ampere
- VRAM (GB)
- 80
- Memory type
- HBM2e
- Form factor
- SXM4
- Memory bandwidth (GB/s)
- 2039
- Maximum board power (W)
- 400
- CUDA cores
- 6912
- Tensor cores
- 432 / 3rd generation
- Ray tracing cores
- Not verified for this edition
- CUDA capability
- 8.0
- Memory ECC
- Not verified for this edition
- Host interface
- PCIe Gen4
- GPU interconnect
- 3rd generation; 600 GB/s aggregate bidirectional
- Hardware partitioning (MIG)
- Up to 7 hardware instances; provider must enable it
- Cooling
- Server platform dependent
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.
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.