Cloud GPU

How to Choose the Right Cloud GPU for Your Workload

Choosing the right cloud GPU depends on three factors: workload type (training vs inference), model size (which determines minimum VRAM), and budget. According to Signwl data, there are 39 GPU and accelerator types available in the cloud, ranging from $0.10/hr budget inference chips to $17+/hr frontier training accelerators. Matching the right GPU to your workload can save 50-80% compared to using an oversized accelerator.

Step 1: Determine Your Workload Type

**Training/Fine-tuning** — You need high compute throughput (TFLOPS) and large memory. Start with H100 for large-scale training, A100 for mid-scale training and fine-tuning, or L40S for lightweight fine-tuning.

**Inference** — You need cost efficiency and sufficient memory for your model. Start with T4 or L4 for small models (≤13B parameters), L40S for medium models (≤25B), or A100/H200 for large models (≤70B+).

Step 2: Check Memory Requirements

Your model must fit in GPU VRAM. Calculate the minimum VRAM needed:

- FP16: ~2 bytes per parameter (7B model = 14GB) - INT8: ~1 byte per parameter (7B model = 7GB) - INT4: ~0.5 bytes per parameter (7B model = 3.5GB)

For training, multiply by 3-4x for gradients and optimiser states. If your model doesn't fit in a single GPU's VRAM, you'll need multi-GPU setups or a higher-memory GPU.

Step 3: Optimise for Budget

Once you've identified GPUs that meet your performance and memory requirements, compare costs:

- Use Signwl's TFLOPS per dollar metric to find the best value - Consider spot pricing for training (60-90% savings) - Deploy in cost-effective regions (North America is typically cheapest) - Right-size: don't use an H100 for a workload that runs fine on an A100

Signwl's GPU comparison pages can help you evaluate options side by side with live pricing data.

Quick Reference Guide

**Budget inference (≤$0.50/hr):** T4, L4, Inferentia **Mid-tier inference ($0.50-2/hr):** A10G, L40S, A100 40GB **Training ($2-8/hr):** A100 80GB, H100, MI300X **Frontier training ($8+/hr):** H200, B200, GB200, GB300

These ranges reflect Signwl's blended pricing across spot, on-demand, and reserved tiers.

Frequently Asked Questions

What is the best GPU for AI training?

The NVIDIA H100 is the most widely used training GPU, offering 990 FP16 TFLOPS and 80GB memory at ~$6/hr blended pricing. For budget training, the A100 80GB at ~$2.50/hr offers strong value. For frontier-scale training, the B200 and GB200 deliver higher performance.

What is the cheapest GPU for AI inference?

The NVIDIA T4 at ~$0.25/hr blended pricing is the cheapest widely available inference GPU with 16GB memory. The L4 at ~$0.45/hr offers better performance with 24GB memory. AWS Inferentia at ~$0.20/hr is the cheapest custom silicon option.

How do I compare GPU value for money?

Use the TFLOPS per dollar metric — divide FP16 TFLOPS by the hourly cost. Higher is better. Signwl calculates this for every GPU. Also consider your specific workload: a GPU with lower TFLOPS but enough memory for your model may be the best value.