NVIDIA Ampere

NVIDIA A100 GPU cloud

Name: NVIDIA A100 SXM GPU Cloud
Brand: NVIDIA
Price: 1.77 USD

Spin up NVIDIA A100 SXM (80GB) GPUs on demand from $1.77/hr, the value pick for fine-tuning, cost-efficient inference, and research on a mature Ampere stack.

from $1.77/hr

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NVIDIA A100 SXM

GPU memory: 80GB HBM2e
Memory bandwidth: 2.0 TB/s

Technical specifications

Architecture: Ampere
GPU memory: 80GB HBM2e
Memory bandwidth: 2.0 TB/s
NVLink: 600 GB/s
FP16/BF16 (Tensor): 624 TFLOPS
Max TDP: 400W
GPUs per node: 8 (HGX A100)

*Peak performance with sparsity, per NVIDIA official specs. Final specs may vary by node configuration.

Pricing & availability

NVIDIA A100 SXMSelf-serve

$1.77per GPU/hr

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What's the A100 best for?

Fine-tuning & LoRA

Fine-tune 7B–70B models cost-effectively. Up to 8 A100s on one node (80GB HBM2e each) cover most LoRA and smaller full fine-tuning jobs, and per-second billing keeps each iteration cheap.

Cost-efficient inference

Serve most open models in production at a lower $/hr than Hopper, with mature TensorRT, vLLM, and Triton support.

Research & teaching

A battle-tested data-center GPU: CUDA, PyTorch, and JAX just work. Spin up A100 GPUs on demand for experiments, coursework, and reproducible runs.

Compare NVIDIA data-center GPUs

	A100 You're viewing	H100 Hopper	H200 Hopper	B200 Blackwell	B300 Blackwell	GB300 Blackwell
Architecture	Ampere	Hopper	Hopper	Blackwell	Blackwell	Blackwell
GPU memory	80GB HBM2e	80GB HBM3	141GB HBM3e	192GB HBM3e	up to 288GB HBM3e	up to 288GB HBM3e
Memory bandwidth	2.0 TB/s	3.35 TB/s	4.8 TB/s	8 TB/s	8 TB/s	8 TB/s
FP8 (Tensor)	Not supported	3,958 TFLOPS	3,958 TFLOPS	9 PFLOPS	9 PFLOPS	10 PFLOPS
Access	from $1.77/hr	from $3.57/hr	Reserved only	Reserved only	Reserved only	Reserved only
Best for	Cost-efficient fine-tuning & inference	Cost-efficient training & inference	Long-context & large-model inference	Frontier-scale training (FP4)	Largest models & reasoning inference	Cluster-scale training & inference

Why AI teams run GPUs on VESSL Cloud

From A100 to Blackwell Ultra

Run A100 and H100 on demand. Reserve H200 and Blackwell GPUs for larger runs, so each workload gets the GPU it needs.

Issues caught early

Every VM Cluster node is health-checked continuously, with its status refreshed about once a minute. Workspaces and Jobs alert you when a run crosses a resource threshold or hits a runtime issue.

From one node to a cluster

Train and serve on up to 8 GPUs on one node, and move to a multi-node VM Cluster when one node is not enough.

Enterprise-ready

SOC 2 Type II compliance, with dedicated support for production AI.

Frequently asked questions

How much does an NVIDIA A100 cost on VESSL Cloud?

A100 SXM (80GB) starts at $1.77/hr on-demand, self-serve at cloud.vessl.ai.

How much VRAM does the A100 have?

The A100 SXM has 80GB of HBM2e memory at 2.0 TB/s bandwidth. For larger models, longer context, or FP8, the H100 (80GB HBM3) and H200 (141GB HBM3e) add more compute and bandwidth.

A100 vs H100: which should I pick?

The A100 is the value pick: a lower $/hr for fine-tuning, inference, and research. The H100 adds FP8 and delivers roughly 3× the A100's FP16/BF16 tensor throughput, for large-scale training and high-throughput serving. Start on the A100 and move up when a run needs it.

Can I run multi-node A100 training?

Not across nodes. A Workspace or Job can use up to 8 A100s on one node. For distributed training across nodes, use a VM Cluster, which runs H100 through B300.