GPU Use Cases
A100 80GB use cases
The A100 80GB is the most widely deployed GPU for production AI workloads. Its 80 GB of HBM2e memory, 2,039 GB/s bandwidth, and mature CUDA ecosystem make it the reliable workhorse for training, inference, and HPC across virtually every cloud provider.
LLM training
The A100 80GB is the most cost-effective GPU for training models in the 7B–30B parameter range. Its 312 TFLOPS of FP16/BF16 throughput and 80 GB HBM2e capacity fit a 40B model at FP16 on a single card. NVLink 3.0 at 600 GB/s enables multi-GPU tensor parallelism for 70B+ model training across 8-GPU DGX A100 nodes. The A100 has the most mature software ecosystem of any data-center GPU — virtually every ML framework and training library has been optimized for it.
LLM inference
The A100 80GB is the most available high-end GPU for inference serving of models up to 70B parameters. Its 80 GB HBM2e fits Llama 3.1 70B at INT8 on a single card. While it lacks the H100's FP8 Transformer Engine, INT8 inference via TensorRT is well-supported and delivers 624 TOPS. For models below 30B parameters, the A100 often provides better cost-per-token than the H100.
HPC workloads
The A100 supports FP64 double-precision at 9.7 TFLOPS for scientific simulation and HPC. Its NVLink 3.0 fabric enables tight multi-GPU coupling for MPI workloads. The A100 is the most widely available HPC GPU across cloud providers, with broad support for CUDA-based scientific computing libraries.
Fine-tuning
The A100 80GB is a strong choice for full fine-tuning of 7B–30B models and LoRA/QLoRA fine-tuning of models up to 70B. Its 80 GB capacity provides headroom for optimizer states, activations, and gradient accumulation. The mature Ampere software ecosystem means most fine-tuning frameworks have been optimized for A100 performance.