Contents
- A100 GPU Specifications
- CoreWeave A100 Pricing
- How to Rent A100 on CoreWeave
- A100 vs Other Providers
- FAQ
- Related Resources
- Sources
A100 GPU Specifications
The NVIDIA A100 Tensor GPU powers most of the world's largest AI workloads. Understanding its core specifications helps determine whether this processor suits a project's needs.
Memory capacity stands at 40GB or 80GB depending on the configuration. The processor delivers peak BF16/TF32 tensor core performance of 312 TFLOPS (19.5 TFLOPS FP32) and supports advanced precision modes including TF32, FP16, and INT8. The memory bandwidth reaches 2 TB/s (SXM), critical for large model training and inference at scale.
The A100 excels at training transformer models, running large language models, and batch processing for computer vision tasks. CoreWeave offers the A100 as an 8-GPU cluster configuration, making it ideal for distributed training jobs that require multiple GPUs working in parallel.
CoreWeave A100 Pricing
CoreWeave's A100 pricing as of March 2026 reflects the provider's data center optimization. An 8-GPU A100 cluster costs $21.60 per hour. This breaks down to approximately $2.70 per individual GPU when divided equally across eight processors.
Compared to purchasing hardware directly, this hourly rate avoids capital expenditure on GPUs that may sit idle during off-peak periods. CoreWeave charges monthly billing with automatic scale-down features to prevent unexpected bills from forgotten instances.
Storage and egress fees apply separately. Users store model checkpoints and datasets on CoreWeave's block storage at approximately $0.07 per GB per month. Data transfer out of CoreWeave incurs charges at typical cloud rates.
The 8-GPU configuration provides better per-GPU rates than renting individual A100s elsewhere. Training large models often requires multiple GPUs for effective distributed training, making the cluster approach more practical than managing single instances.
How to Rent A100 on CoreWeave
Setting up an A100 cluster on CoreWeave involves several straightforward steps. First, create an account at coreweave.com and set up billing information. CoreWeave accepts most major credit cards and supports monthly invoicing for qualified customers.
After account setup, navigate to the cloud dashboard and select "Create Cluster." Choose the A100 8-GPU configuration from the available hardware options. Select a geographic region closest to the user's location to minimize latency for data transfer.
Configure the instance type and operating system. Most users run Ubuntu 22.04 LTS with NVIDIA's CUDA drivers pre-installed. PyTorch, TensorFlow, and other ML frameworks can be installed via package managers immediately after provisioning.
To connect to the cluster, download the SSH key from the CoreWeave dashboard and store it locally with restricted permissions. Connect using SSH from a terminal: ssh -i key.pem ubuntu@instance-ip. File transfer works through SCP or by mounting cloud storage volumes.
CoreWeave provides persistent storage options that remain available even after instances shut down. This allows training runs to resume from the last saved checkpoint without re-uploading datasets each time.
A100 vs Other Providers
The A100 appears on most major GPU cloud platforms, but pricing and availability differ significantly. RunPod offers A100 PCIe at $1.19 per hour and A100 SXM at $1.39 per hour for single-GPU rental. For users needing only one A100, RunPod provides better economics than CoreWeave's 8-GPU cluster.
Lambda Labs prices individual A100 PCIe GPUs at $1.48 per hour, above RunPod's PCIe rate and competitive for single-GPU workloads. Lambda includes higher-speed network connectivity and stronger SLA guarantees.
For distributed training requiring eight A100s, CoreWeave's $21.60 per hour cluster becomes cost-effective. The per-GPU rate of $2.70 on CoreWeave seems high for single GPUs, but the benefit comes from pre-configured multi-GPU clusters with optimized inter-GPU networking.
AWS provides A100 instances through EC2, though pricing fluctuates based on instance type and region. Azure offers similar options with comparable or higher costs. Both public cloud providers charge premium rates compared to specialized GPU rental platforms.
FAQ
Can I rent a single A100 from CoreWeave instead of an 8-GPU cluster? CoreWeave's standard offering is the 8-GPU cluster configuration. For single A100 GPUs, RunPod or Lambda provide more economical options.
What happens to my work if I exceed my account's credit limit? CoreWeave pauses instances when credit is exhausted to prevent surprise charges. Users receive email warnings when balance drops below a threshold.
Do I need experience with distributed training to use the 8-GPU cluster? The cluster provides standard networking between GPUs, but users should understand distributed training frameworks like PyTorch Distributed or TensorFlow Distributed Data Parallel. Single-GPU training runs work without modification.
How does CoreWeave's network performance compare to other providers? CoreWeave uses dedicated networking for multi-GPU communication, achieving sub-millisecond latency between cluster nodes. This supports demanding distributed training workloads.
What storage options exist for large datasets? CoreWeave provides block storage, NFS mounts, and S3-compatible object storage. Block storage integrates most smoothly with GPU instances.
Related Resources
H100 Specifications and Performance provide higher memory and compute for the most demanding workloads.
GPU Pricing Comparison Guide breaks down costs across all major providers for different use cases.
A100 Specifications and Benchmarks dig deeper into the A100's technical details and real-world performance.