Choosing GPUs: Comparing H100, A100, L40S & Next-Gen Models

Which GPU should I use to run my AI Model?

The landscape of AI development is constantly evolving, and nowhere is this more evident than in the ever-expanding range of GPUs tailored for high-performance computing. Whether you're training massive language models, fine-tuning diffusion models, or running complex inference pipelines, choosing the right GPU can directly impact your costs, performance, and productivity.

This guide breaks down the differences between popular options like the NVIDIA H100, A100, L40S, and next-gen GPUs, helping you decide what best suits your workload. Along the way, we’ll reference RunPod’s infrastructure for seamless deployment, containerized environments, and scalable compute.

👉 Sign up for RunPod to launch your AI container, inference pipeline, or notebook with GPU support.

Why GPU Choice Matters for AI & ML Workflows

Every AI use case—be it LLM training, image generation, video processing, or inferencing—demands a different GPU profile. Selecting a GPU that over- or under-performs your requirements can lead to budget waste or bottlenecks.

Key Considerations:

Compute Power (TFLOPs)
VRAM and Memory Bandwidth
Tensor Core Support
Power Efficiency
Availability and Cost

Thankfully, RunPod GPU Templates make it easy to benchmark and deploy containers across GPU types without vendor lock-in or expensive setup overhead.

NVIDIA A100: The Workhorse for General AI Training

The NVIDIA A100 has become a foundational chip for AI research and commercial ML applications. Launched as part of the Ampere architecture, it’s especially strong in multi-GPU training and large batch operations.

Specs Summary:

40 GB or 80 GB of HBM2e memory
19.5 TFLOPs (FP32) / 312 TFLOPs (TensorFloat-32)
NVLink and PCIe versions
Available widely on RunPod Pricing tiers

Ideal For:

Foundation model training (GPT-3 scale)
Image-to-image training in diffusion models
Vision transformer (ViT) pretraining

Deployment Tip: Use RunPod's Container Launch Guide to spin up a container with PyTorch or TensorFlow on A100 with pre-configured environments.

NVIDIA H100: Ultimate Performance for Next-Gen AI

The H100, part of NVIDIA’s Hopper architecture, delivers 3–6x the performance of the A100 for certain workloads. It’s optimized for transformer-heavy models like LLaMA 3, Mistral, and GPT-4 derivatives.

Specs Summary:

80 GB HBM3 memory (up to 3.35 TB/s bandwidth)
60 TFLOPs FP32 / 1,000+ TFLOPs Tensor Performance
NVLink and SXM options
Supports FP8 precision, maximizing memory use

Ideal For:

Generative AI at scale (LLMs, diffusion, audio)
Production inference with heavy batch throughput
Multi-GPU transformer training

On RunPod, H100s are available in select high-performance clusters. For scaling and automation, check the RunPod API Documentation.

NVIDIA L40S: Balanced Option for Vision & GenAI Inference

While not as powerful as the H100 or A100, the L40S, based on the Ada Lovelace architecture—strikes an efficient balance between price and performance. It's built for AI inferencing, media generation, and workstation-level workloads.

Specs Summary:

48 GB GDDR6 ECC memory
91.6 TFLOPs FP32 performance
366 TOPS INT8 / 183 TFLOPs FP16
PCIe Gen 4 x16 connectivity

Ideal For:

Inference of LLMs and diffusion models
Small to medium fine-tuning tasks
Real-time rendering or multimedia AI

The L40S shines when running containerized inference pipelines, especially with model parallelization. See RunPod’s Model Deployment Examples to get started with Stable Diffusion XL, Whisper, or T5.

Next-Gen GPU Models: What's Coming & Should You Wait?

NVIDIA and AMD are pushing the envelope with newer GPU architectures. Rumored successors like the B100 and Rubin architecture are expected to offer:

More memory bandwidth (>4 TB/s)
Enhanced AI-dedicated cores
Optimized efficiency for edge-cloud inferencing

Key Question: Should you wait or deploy now?

Unless you’re planning infrastructure for 2026 and beyond, A100 and H100 remain the gold standard for scalable cloud AI. With RunPod’s hourly GPU pricing, you’re never locked into hardware—spin up what you need when you need it.

Cost & Availability Comparison on RunPod

GPU	Memory	Ideal Use Case	Performance Tier	Availability	Cost Efficiency
A100	40–80GB	Large model training	High	Widely available	High
H100	80GB	SOTA LLM training & inference	Premium	Limited clusters	Medium
L40S	48GB	Inference & media pipelines	Mid	Common	High
Others (3090, 4090, etc.)	24GB	Dev & testing	Entry	Very common	Very high

💡 Tip: Visit RunPod’s Pricing Page to see up-to-date GPU hourly rates.

How to Choose the Right GPU for Your Workflow

1. Training vs Inference

For training, go with A100 or H100.
For inference, L40S or 4090 is more cost-efficient.

2. Model Size

LLaMA 2/3, Mixtral, and Falcon models require 40–80GB VRAM for training. H100s work best here.
Smaller models (like BERT, Whisper, or YOLOv8) run smoothly on L40S or even 3090s.

3. Budget

If you’re doing long-term training runs, cheaper clusters using A100s on RunPod can save you thousands over time.

4. Multi-GPU Scaling

Use the RunPod Dockerfile Templates to build scalable environments that utilize multiple GPUs efficiently.

Real-World Use Cases on RunPod

Training LLaMA 3 with H100

A user scaled 8x H100s across RunPod’s secure cluster to fine-tune LLaMA 3. With native NCCL support and Dockerfile optimization, they achieved a 30% speed boost over on-prem setups.

SDXL Inference on L40S

Another user containerized Stable Diffusion XL and deployed it on L40S. With batch size tuning and token streaming, they optimized for both latency and cost.

Whisper Transcription on A100

For audio-to-text at scale, a research group launched PyTorch-based Whisper pipelines on A100, integrating with the RunPod API for auto-scaling across regions.

Best Practices for Deployment

Use Containerized Environments
Reference the Container Launch Guide to launch your preferred framework (Hugging Face, FastAPI, etc.)

Optimize with FP16/FP8
Modern GPUs like H100 and A100 support mixed precision—saving memory and speeding up training.

Pre-Pull Models
Save bandwidth and cold start time by using model deployment templates.

Use the RunPod CLI or API
Automate cluster creation and monitor GPU usage via RunPod's API docs.

External Resources

Want to dive into low-level specs and open-source tooling?
Check out the official NVIDIA CUDA Toolkit on GitHub

FAQs: GPU Deployment on RunPod

What pricing tiers are available for different GPUs on RunPod?

RunPod offers on-demand hourly pricing for A100, H100, L40S, and more. Visit the pricing page to compare availability and region-based rates.

What are the container size or runtime limits?

There are no fixed container runtime limits. Users can configure custom containers with specific memory/GPU requirements and scale up as needed.

Which GPU is best for training a 7B+ parameter LLM?

The H100 is ideal for large model training. However, A100 80GB offers a strong balance of memory and cost for 7B–13B parameter models.

Are L40S GPUs compatible with large transformer models?

Yes. L40S works well for inference on transformer models like BERT, T5, or even LLaMA with quantization. It may not have enough VRAM for training large models from scratch.

Is there a setup walkthrough for Docker deployments?

Yes, RunPod provides a full Dockerfile best practices guide to build optimized containers for training or inference.

How do I monitor GPU usage and optimize costs?

Use the built-in dashboard or RunPod CLI tools to monitor GPU usage, memory, and runtime stats. You can also script auto-shutdowns or scaling via the API.

Final Thoughts

Selecting the right GPU doesn’t have to be overwhelming. Whether you're launching your first notebook or scaling across multi-GPU clusters, RunPod offers flexibility, performance, and affordability.

Experiment across A100, H100, and L40S environments—without provisioning hardware or overpaying for idle resources.

👉 Sign up now to launch your AI container, inference pipeline, or model notebook with full GPU support.