NVIDIA A100 and A100 SXM on Koyeb
Overview
The NVIDIA A100 Tensor Core GPU is a proven workhorse for large-scale AI, data analytics, and high-performance computing (HPC). Powered by NVIDIA Ampere architecture, the A100 can deliver up to 20x higher performance than the previous Volta generation, while also supporting Multi-Instance GPU (MIG) technology to partition a single GPU into up to seven isolated instances for maximum flexibility.
With up to 80GB of HBM2e memory and an industry-leading 2TB/s memory bandwidth, the A100 is built for training and inference of massive models, as well as large-scale simulation workloads.
Best-Suited Workloads
- Large-Scale AI Training: Train massive deep learning models, including foundation models, recommender systems, and vision models.
- Generative AI: Run diffusion models and large transformers efficiently at scale.
- Data Analytics: Accelerate data processing pipelines and SQL-on-GPU workloads.
- High-Performance Computing (HPC): Scientific computing, simulations, and numerical modeling.
- Elastic GPU Partitioning: Run multiple smaller jobs simultaneously using MIG for maximum resource efficiency.
NVIDIA A100 PCIe vs. A100 SXM on Koyeb
When deploying workloads on Koyeb, you may have the choice between NVIDIA A100 PCIe and NVIDIA A100 SXM GPUs. Both leverage the Ampere architecture and deliver exceptional AI and HPC performance, but they differ in form factor, power, interconnects, and scalability. This guide explains the key differences and helps you decide which option best suits your workloads.
The NVIDIA A100 Tensor Core GPU accelerates AI training, inference, data analytics, and HPC applications.
- Both PCIe and SXM variants support 40GB or 80GB of HBM2e memory, Tensor Cores, and MIG (Multi-Instance GPU) partitioning.
- The choice between PCIe and SXM primarily comes down to performance scaling vs. deployment flexibility.
Key Differences
1. Memory Bandwidth
- PCIe A100: ~1,935 GB/s
- SXM A100: ~2,039 GB/s
SXM provides slightly higher memory bandwidth, which benefits memory-bound workloads like large-scale training.
2. Power and Cooling
- PCIe A100: ~250–300 W TDP
- SXM A100: ~400 W TDP
The SXM form factor allows higher sustained power and thermal efficiency, enabling better performance under heavy workloads.
3. GPU-to-GPU Interconnect
- PCIe A100: Relies on PCIe Gen4 (64 GB/s). Optional NVLink bridges connect a small number of GPUs.
- SXM A100: Full NVLink integration with up to 600 GB/s GPU-to-GPU bandwidth.
SXM is superior for multi-GPU training and distributed HPC workloads where inter-GPU communication is critical.
4. Form Factor and Deployment
- PCIe A100: Standard PCIe x16 card, compatible with a wide variety of servers. Easier to integrate into mixed or modular environments.
- SXM A100: Custom SXM socket, available in specialized systems (e.g., NVIDIA HGX or DGX). Offers higher density and optimized cooling.
PCIe = flexibility, SXM = high-density scale-up.
Workload Suitability on Koyeb
| Workload Type | Best Choice | Why |
|---|---|---|
| Large-Scale AI Training (LLMs, Transformers) | A100 SXM | Higher bandwidth, full NVLink interconnect, higher sustained power for dense multi-GPU training. |
| HPC Simulations (Climate, Genomics, Physics) | A100 SXM | NVLink + high TDP deliver faster inter-GPU compute and scaling. |
| Inference at Scale | A100 PCIe | Cost-efficient, lower power draw, flexible deployment. |
| Fine-Tuning / Small to Mid-Size Models | A100 PCIe | MIG partitioning enables multiple jobs per GPU, optimizing costs. |
| Mixed Workloads Across Teams | A100 PCIe | Easier integration, resource isolation with MIG, fits modular cloud environments. |
Why Deploy on Koyeb?
Koyeb provides a serverless GPU platform that lets you scale A100-powered workloads on-demand. By running your A100 workloads on Koyeb, you get:
- On-Demand Elasticity: Dynamically scale training and inference jobs across multiple A100 instances.
- Pay-as-You-Go Pricing: Optimize cost by running workloads only when needed.
- Global Infrastructure: Low-latency access to GPUs across multiple regions.
- Integrated Model Deployment: Serve models directly on the same platform you train them, streamlining MLOps.
The A100 on Koyeb is the right choice if you’re running large-scale training, high-throughput inference, or HPC workloads that demand raw compute and memory bandwidth. To compare A100 performance to other available GPUs, view the GPU Benchmarks documentation