Massed Compute

The NVIDIA A100 80GB keeps doing the heavy lifting for AI teams. It has the memory to fine-tune today’s popular models, the bandwidth to feed them, the NVLink to scale them, and the FP64 to run real science. At Massed Compute, A100 80GB instances start at $1.35 per hour on-demand.

This post covers what the card is, the four workloads where it performs best, how its price compares to the big clouds, and when a different GPU is the better call.

What the A100 Actually Is

The NVIDIA A100 is a data center GPU built on the Ampere architecture with third-generation Tensor Cores. Massed Compute runs the 80GB version, which pairs 80GB of HBM2e memory with about 1.9 to 2.0 TB/s of memory bandwidth. That memory and bandwidth are what let it handle models that overwhelm smaller cards.

The A100 supports TF32 and BF16 for fast mixed-precision training, full FP64 Tensor Cores for double-precision science, NVLink at 600 GB/s for multi-GPU scaling, and Multi-Instance GPU for splitting one card into as many as seven isolated workers. It comes in PCIe, SXM4, and DGX configurations, so you can start on a single card and scale to a full eight-GPU node.

For lighter inference, image generation, and rendering that fit inside 48GB, the L40 gives you a lower price. When the job needs 80GB, NVLink scaling, or real double precision, the A100 is built for it.

Fine-Tuning Today’s Popular Models

80GB of HBM2e clears the memory wall that stops a 48GB card. QLoRA and LoRA fine-tunes of 70B-class models like Llama 3.3 70B and DeepSeek-R1-Distill-70B fit on a single A100, and full fine-tunes of 7B to 8B models like Llama 3.1 8B and Qwen3 8B fit cleanly too. Third-generation Tensor Cores with TF32 and BF16 keep those runs fast in mixed precision.

A common setup is a QLoRA fine-tune of a 70B model on one A100 80GB with a large batch size and long sequences. On a 48GB card you fight for headroom. On the A100 that headroom is there from the start.

Multi-GPU Distributed Training

A full fine-tune of a 70B model is a bigger job than any single card can hold, so it spreads across a multi-GPU node. The A100 comes in NVLink and SXM4 configurations that connect cards at 600 GB/s, and that fast link keeps large training runs efficient as you add GPUs. You can prototype on one A100, then move the same job to a 4x or 8x node when it is time to scale.

A typical path is to validate a training script on a single card, then launch a DGX A100 node for the full run. Eight A100 80GB GPUs on one machine give you 640GB of GPU memory working together. When the run finishes, you shut it down and stop paying.

High-Throughput Inference at Scale

80GB serves today’s mid-size models in full precision, including Qwen3 32B, Gemma 4 31B, and DeepSeek-R1-Distill-32B, along with 70B-class models when quantized. When request volume climbs, that headroom keeps latency steady. Multi-Instance GPU lets you split one A100 into as many as seven isolated instances, so you can pack many smaller inference jobs onto a single card and keep utilization high.

A team serving several models at once can carve one A100 into MIG slices, give each model its own slice, and run them side by side. Higher utilization means a lower cost per request. For the largest mixture-of-experts models like DeepSeek R1 and Qwen3 235B, a multi-GPU A100 node gives you the combined memory to serve them.

HPC and Scientific Computing

The A100 has full FP64 Tensor Core performance, which most inference-focused GPUs leave out. That makes it a strong fit for double-precision work like genomics, molecular dynamics, computational fluid dynamics, weather modeling, and financial simulation. Research teams get data-center-grade double precision at an hourly rate that fits a grant budget.

A research team can spin up an A100 for a double-precision run, get workstation-class FP64 on cloud hardware, and release it when the job is done. There is no need to buy and maintain a local cluster for compute you only need some of the time.

How the Price Stacks Up

Here is how the A100 80GB on Massed Compute compares to the same card at other clouds as of July 2026. Prices are on-demand rates for a single 80GB A100, with the AWS figure shown per GPU from its eight-GPU node.

The A100 80GB at Massed Compute comes in well below the big-cloud rate for the same card. You get 80GB of data center GPU for serious training work at a price that fits a real budget.

When to Pick a Different GPU

The A100 is not the right answer for every job. If your work fits inside 48GB and leans toward lighter inference, image generation, or rendering, the L40 gives you a lower price for that job. If you want the newest architecture with FP4 inference and fast ray tracing on one card, the RTX PRO 6000 Blackwell is a strong pick.

For the jobs the A100 is built for, though, it stays the workhorse. When the work needs 80GB of memory, NVLink scaling across a multi-GPU node, or real FP64 double precision, the A100 delivers proven data center performance at a price that fits a real budget.