Explore AI Hardware Architectures
Discover the perfect GPU for your workload. Compare availability and pricing across 100+ cloud providers for NVIDIA H100s, AMD MI300X, and alternative silicon.
NVIDIA GPUs (28)
A100
H100
L40S
RTX 4090
RTX 3090
t4
V100
A6000
NVIDIA A100
NVIDIA H100
H200
NVIDIA T4
L4
NVIDIA RTX Series
RTX 4000
RTX A6000
A40
A5000
NVIDIA A10
NVIDIA A10G
NVIDIA H200
NVIDIA RTX 4090
NVIDIA V100
RTX 4090 (Decentralized)
RTX A5000
rtx-4000-ada
rtx-6000-ada
Tesla T4
AMD Accelerators (2)
Intel Architecture (1)
Alternative & Custom Silicon (8)
Cloud & Managed Environments (1)
How to Choose the Right AI Hardware
Selecting the correct GPU architecture is critical for balancing performance and cloud costs. Workloads generally fall into two categories: Training and Inference.
For LLM Training & Fine-Tuning: You need maximum VRAM bandwidth and high-speed interconnects. Flagship GPUs like the NVIDIA H100 (SXM5) or AMD MI300X are industry standards. They allow massive model weights to be distributed across clusters without bottlenecking.
For Inference & General Compute: Once a model is trained, running it requires significantly less compute. Opting for inference-optimized silicon like the NVIDIA L40S, L4, or even the RTX 4090 can reduce your hourly cloud spend by over 70% while maintaining sub-second latency.
Frequently Asked Questions
What is the difference between H100 PCIe and H100 SXM5?
The SXM5 variant of the H100 connects directly to the motherboard using NVIDIA's NVLink, providing up to 900 GB/s of bandwidth between GPUs. This makes it significantly faster for distributed training compared to the PCIe version, which is limited by the PCIe bus speed.
Can I use consumer GPUs like the RTX 4090 for AI?
Yes, consumer GPUs like the RTX 4090 and RTX 3090 offer incredible price-to-performance ratios for smaller workloads, inference, and prototyping. However, their end-user license agreements restrict deployment in large commercial data centers, meaning you will typically find them offered by specialized boutique cloud providers.
What are alternative AI accelerators?
Beyond NVIDIA, the AI hardware ecosystem is rapidly expanding. AMD's MI300X offers comparable performance to the H100. Additionally, custom ASICs and LPUs from companies like Groq, Cerebras, and Intel (Gaudi 3) offer specialized architectures designed purely to accelerate neural networks at a fraction of the power cost.