Rent NVIDIA A100 80GB GPUs on Demand from $0.45/hr

On Spheron the A100 80GB starts at $0.45/hr per GPU per hour on dedicated (99.99% SLA, non-interruptible), with interruptible spot instances cheaper still. There is no minimum commit and billing is per minute. For reference, Lambda Labs runs ~$2.49/hr, AWS p4de ~$3.43/hr per GPU, Azure ND A100 v4 ~$4.10/hr per GPU, and Google Cloud a2-ultragpu around $5/hr.

Spot instances on Spheron are the cheapest path, often 50 to 70 percent below the dedicated rate. The trade-off is that the instance can be reclaimed when demand spikes, so checkpoint every 15 to 30 minutes and treat spot as a fit for fault-tolerant training, batch jobs, and experimentation. For steady production serving, stay on dedicated (99.99% SLA, non-interruptible). Both are on-demand tiers with per-minute billing.

Yes. Spheron bills per minute with no minimum. A one-hour benchmark costs you one hour. No contracts, no reserved-instance lock-in on dedicated or spot, and no commit fees.

Most A100 instances are live in 45 to 90 seconds. Hardware is pre-warmed, so provisioning behaves more like a container start than a VM boot. If your Docker image is ready, you can be running a training script inside two minutes of hitting deploy.

SXM4 is the higher-power variant (400W) with NVLink between GPUs at 600 GB/s, which matters for multi-GPU training and model parallelism. PCIe is lower-power (300W) and easier to mix with standard servers, but has no NVLink. Pick SXM for distributed training or 70B FP16 inference across 2+ GPUs. Pick PCIe for single-GPU inference or data processing.

The 80GB variant doubles VRAM and bumps memory bandwidth from 1.55 TB/s to 2.0 TB/s. That matters for larger batch sizes, long-context inference, and 70B-class quantized models. Spheron defaults to the 80GB SKU because the memory headroom usually pays for itself.

Yes. A single A100 splits into up to 7 isolated MIG instances, each with dedicated compute, memory, and bandwidth. MIG is perfect for running multiple small inference workloads on one card without noisy-neighbor effects. It is exposed on both SXM and PCIe variants.

Yes. Spheron offers 8x A100 per node with NVLink, and multi-node clusters connected by 200 Gb/s HDR InfiniBand with GPUDirect RDMA. Clusters are tested with PyTorch DDP, DeepSpeed ZeRO-3, and Megatron-LM. Larger configurations are available on request.

A100 capacity is online across North America, Europe, and Asia, sourced from data center partners. Availability shifts with demand and the dashboard shows live capacity per region.

PyTorch, TensorFlow, JAX, and the major serving stacks (vLLM, TensorRT-LLM, Triton, SGLang) all ship in the default images. CUDA 12.6+, cuDNN, NCCL, and RAPIDS are pre-tuned for A100. You can also bring your own Docker image.

For inference, yes. A 70B model in INT4 (~35GB) runs on a single A100 80GB. At INT8 you need two A100 80GBs with tensor parallelism. FP16 training or inference at 70B requires 2+ A100 80GB with NVLink. Sweet spot for the A100 remains 7B to 30B parameters.

If your workload is FP8-native or memory-bandwidth-bound, H100 pays for itself. If you are doing classic training or fine-tuning up to 30B parameters, or inference on models that fit in 80GB without FP8, A100 usually wins on dollars per token. Start on A100, move to H100 when the speedup justifies the cost.

For 100+ GPU deployments and production-critical workloads, Spheron offers dedicated Slack or Discord support, sourcing assistance, and SLA-backed instances. Smaller deployments are self-serve through the dashboard.

For the same A100 80GB hardware, Spheron is meaningfully cheaper than AWS p4de, Azure ND A100 v4, and GCP a2-ultragpu on-demand. As of April 2026, hyperscaler on-demand A100 80GB pricing runs roughly $3.43/hr per GPU on AWS p4de, $4.10/hr on Azure ND A100 v4, and about $5/hr on GCP. Spheron starts at $0.45/hr. Same silicon, different pricing model.