NVIDIA Ampere
NVIDIA A100 GPU Cloud & Dedicated Infrastructure
Cost-efficient, widely available GPU for training and fine-tuning.
The NVIDIA A100 remains a cost-efficient option for training, fine-tuning, and inference workloads that do not require Hopper- or Blackwell-generation throughput. Its broad availability across providers often makes it a practical choice for development, experimentation, and steady-state production workloads.
A100 specifications
- GPU Memory
- 40GB or 80GB HBM2e
- Memory Bandwidth
- Up to ~2 TB/s (80GB variant)
- Architecture
- NVIDIA Ampere
- Interconnect
- NVLink / NVSwitch, PCIe Gen4
- Form Factors
- SXM and PCIe variants
- Typical Server Configs
- 1x, 2x, 4x, 8x GPU nodes
Deployment options
- On-demand cloud GPU instances
- Reserved capacity
- Dedicated A100 servers
- Multi-GPU A100 nodes
Best suited for
- Fine-tuning small to mid-sized models
- Development and staging environments
- Cost-sensitive inference workloads
- Teams migrating existing Ampere-era workloads without re-architecting
Less ideal for
- Serving very large models where memory bandwidth is the primary constraint
- Workloads that depend on FP8 Transformer Engine support
Related solutions
A100 frequently asked questions
Is A100 outdated for AI workloads in 2026?
No. The A100 remains a practical, cost-efficient option for many training, fine-tuning, and inference workloads, particularly where Hopper- or Blackwell-generation throughput is not required. It is often the most economical way to run steady-state production workloads.
Should I choose the 40GB or 80GB A100 variant?
The 80GB variant is generally recommended for LLM workloads and larger batch sizes, while the 40GB variant can be sufficient for smaller models and development environments. We can help size this based on your specific model and throughput requirements.
Can I mix A100 and H100 capacity across my infrastructure?
Yes. Many customers run development and lower-priority workloads on A100 while reserving H100 or H200 capacity for latency-sensitive production inference. We can help design and operate a mixed-GPU infrastructure strategy.