Rent NVIDIA A100 80GB cloud GPU
The proven data center GPU that launched the AI revolution. 6,912 CUDA cores with 80GB of HBM2e, starting at $1.10/hr on OpenRelay.
NVIDIA A100: the workhorse of modern AI infrastructure
The NVIDIA A100 is the data center GPU that powered the AI revolution. Released in 2020 as the flagship of the Ampere data center lineup, it became the default choice for AI training and inference at every major tech company, research lab, and cloud provider. From GPT-3 to Stable Diffusion, the A100 was the hardware behind the breakthroughs that brought AI into the mainstream.
What makes the A100 special is its HBM2e memory. With 80GB of high-bandwidth memory delivering 1,935 GB/s of throughput, the A100 can handle models that are too large for consumer GPUs with their 24GB GDDR6X. This makes it essential for running unquantized 13B-34B parameter models, for training workloads that require large batch sizes, and for scientific computing applications that need fast double-precision performance.
On OpenRelay, the A100 80GB is $1.10/hr. With no egress fees, per-minute billing, and built-in auto-failover, you get enterprise-grade A100 compute without the enterprise procurement process. Whether you're running production inference, training custom models, or conducting research, OpenRelay delivers A100 access at startup-friendly prices.
A100 80GB technical specifications
| Specification | A100 80GB |
|---|---|
| GPU Architecture | Ampere (GA100) |
| VRAM | 80GB HBM2e |
| CUDA Cores | 6,912 |
| Memory Bandwidth | 1,935 GB/s |
| FP32 Performance | 19.5 TFLOPS |
| FP16 (Tensor) | 312 TFLOPS (with sparsity: 624) |
| TF32 (Tensor) | 156 TFLOPS (with sparsity: 312) |
| FP64 (Double) | 9.7 TFLOPS |
| TDP | 400W |
| Memory Bus | 5120-bit |
| Tensor Cores | 432 (3rd Gen) |
| NVLink | 3rd Gen, 600 GB/s |
| Multi-Instance GPU (MIG) | Yes (up to 7 instances) |
The A100 introduced several features that defined modern AI hardware: TF32 for automatic training speedups without code changes, structured sparsity for 2x throughput on compatible models, 3rd-gen NVLink for multi-GPU scaling, and Multi-Instance GPU (MIG) for splitting a single A100 into up to 7 isolated GPU instances. These features made the A100 the most versatile data center GPU of its generation.
A100 cloud GPU pricing on OpenRelay
Best use cases for the A100
The A100 occupies a unique position in the GPU landscape: it's the most cost-effective way to access HBM memory and data center features without stepping up to H100 pricing. Here are the workloads where it delivers the best value:
Medium-to-Large Model Inference (13B-34B)
Run 13B to 34B models such as CodeLlama 34B at FP16 without quantization, or Mixtral 8x7B in 8-bit. The 80GB HBM2e provides enough memory for these models while the high bandwidth (1,935 GB/s) ensures fast token generation. For workloads where quantization would reduce output quality, the A100 lets you serve models at full precision at a reasonable cost.
Mixed-Precision Model Training
Fine-tune 13B models with LoRA, or 70B models with QLoRA. The A100's TF32 Tensor Cores automatically accelerate training without code changes, and BF16 support ensures numerical stability for large-scale training runs. 3rd-gen NVLink enables efficient multi-GPU training for larger models.
Large Batch Inference
Process high volumes of requests with continuous batching frameworks like vLLM and TGI. The A100's 80GB memory allows for larger batch sizes than 24GB consumer GPUs, increasing throughput per GPU. For production APIs handling thousands of concurrent requests, the A100's batch processing capabilities can serve more users per dollar than running multiple smaller GPUs.
Scientific Computing & HPC
The A100 delivers 9.7 TFLOPS of FP64 (double-precision) performance, making it one of the best GPUs for scientific computing workloads. Molecular dynamics, computational fluid dynamics, climate simulations, and genomics analysis all benefit from the A100's combination of high FP64 throughput and 80GB HBM memory. The 1,935 GB/s memory bandwidth keeps data flowing for memory-intensive simulations.
Multi-Modal AI & Computer Vision
Deploy vision-language models, large vision transformers, and multi-modal systems that require more than 24GB VRAM. Models like CLIP ViT-L, SAM (Segment Anything) with high-resolution inputs, and video understanding models benefit from the A100's generous memory and bandwidth. The 80GB capacity enables larger input resolutions and batch sizes for vision workloads.
Development & Experimentation
The A100 is the industry standard GPU for ML development. Every major framework (PyTorch, TensorFlow, JAX) is optimized for A100, every tutorial and benchmark uses A100 numbers, and every deployment guide assumes A100 compatibility. Using an A100 for development ensures your models will work seamlessly when you scale to production, no hardware-specific surprises.
How to deploy an A100 on OpenRelay
OpenRelay makes A100 access as simple as renting a consumer GPU. No enterprise sales calls, no capacity negotiations, no months-long procurement. Here's how to get started:
Create your account
Sign up at openrelay.inc/get-started and add a card. You'll get immediate access to the dashboard and CLI, with the A100 available in the GPU catalog.
Select your A100
Choose the A100 80GB ($1.10/hr) from the GPU catalog. Configure the number of GPUs, region, and your Docker container image. OpenRelay supports all major ML serving frameworks including vLLM, Text Generation Inference, Triton Inference Server, and custom containers.
Deploy with full hardware access
OpenRelay provisions your A100 with VFIO GPU passthrough in a lightweight VM. You get full, exclusive access to the GPU: the full 80GB of HBM2e memory, all 432 Tensor Cores, NVLink connectivity, and MIG capability. No GPU sharing, no virtualization overhead, bare-metal performance with container security.
Monitor and scale
Your A100 deployment comes with built-in auto-failover, load balancing, and real-time monitoring. View GPU utilization, memory usage, and throughput from the dashboard. Add more GPUs as demand grows, or scale down when it drops. Per-minute billing means you only pay for actual usage, no wasted compute.
A100 vs RTX 4090 vs H100: choosing the right GPU
Each GPU serves different needs. Here's a comprehensive comparison to help you make the right choice:
| Feature | RTX 4090 | A100 80GB | H100 SXM |
|---|---|---|---|
| VRAM | 24GB GDDR6X | 80GB HBM2e | 80GB HBM3 |
| Memory BW | 1,008 GB/s | 1,935 GB/s | 3,350 GB/s |
| FP32 | 82.6 TFLOPS | 19.5 TFLOPS | 67 TFLOPS |
| Best for | Fast inference ≤24GB | Training + 80GB models | Large-scale AI |
| NVLink | No | Yes (600 GB/s) | Yes (900 GB/s) |
Choose the A100 when: You need 80GB of VRAM for models that don't fit in 24GB, you're doing mixed-precision training with TF32 or BF16, you need NVLink for multi-GPU workloads, you require MIG for multi-tenant GPU sharing, or you need FP64 performance for scientific computing. The A100 is the middle ground between consumer GPU value and cutting-edge H100 performance.
Frequently asked questions
How much does it cost to rent an A100 on OpenRelay?
OpenRelay offers the NVIDIA A100 80GB at $1.10 per hour with per-minute billing. Running it 24/7 works out to approximately $792 per month. There are no minimum commitments, no egress fees, and no hidden costs.
What is the A100 best used for?
The A100 excels at mixed-precision training, large batch inference, and workloads that need more than 24GB VRAM. Its 80GB HBM2e runs 13B to 34B models such as CodeLlama 34B at FP16, and its high memory bandwidth (1,935 GB/s) makes it excellent for memory-bound inference tasks. It's also widely used in HPC and scientific computing for double-precision workloads.
Can I run Llama 3 70B on a single A100?
Not at FP16, which requires ~140GB. The A100 80GB fits 70B models at 8-bit quantization (~70GB), or with INT4 quantization (GPTQ/AWQ) at approximately 35-40GB. For full-precision 70B inference, use a multi-GPU A100 setup.
Does OpenRelay offer multi-GPU A100 deployments?
Yes. OpenRelay supports multi-GPU A100 deployments with automatic load balancing and failover. You can scale horizontally across multiple A100 nodes for distributed inference or distributed training workloads. NVLink-connected multi-GPU configurations are available for workloads that require high-bandwidth GPU-to-GPU communication.
Ready to deploy on the A100?
Enterprise-grade GPU compute at startup-friendly prices. No egress fees. No minimum commitments. Deploy in minutes, not weeks.