How to choose a GPU for your tasks

Choosing the right Nvidia GPU for AI work isn’t about the biggest number; it’s about matching silicon to workload and budget. Here’s a guide to H200, L40S and L4. Fiberax have each of them in  infrastructure.

H200 — heavy hitter

If you’re serving large LLMs, pushing long contexts, or building dense RAG pipelines, H200 is the safe bet. NVLink enables fast inter-GPU communication so multi-GPU models behave like one larger accelerator. You get huge memory bandwidth and low latency under load — ideal for enterprise chatbots, multilingual NLP and retrieval in a private cloud. Trade-offs: premium pricing and power draw. Use it when latency SLOs matter most.

L40S — versatile workhorse

L40S shines for mixed-precision throughput across NLP and CV. It’s great for multi-tenant inference, moderate fine-tuning, vector pre-processing, and image/video tasks. If your roadmap spans text images and multimodal features, L40S offers a balanced profile.

L4 — efficient scale-out

L4 is the efficiency play: lower power, compact form factor, and strong value for video analytics, lighter NLP and microservices that scale horizontally. Use L4 for streaming CV (detectors, trackers), real-time captioning, lightweight RAG, and high-fanout APIs where autoscaling keeps bills in check. Ideal when cost per token or per frame is king.

How to choose GPU — a quick rubric

• Prioritise latency on big models or long prompts? H200.

• Need one GPU for varied AI tasks across NLP and CV? L40S.

• Targeting cost-efficient, many-instance inference at the edge or in containers? L4.

Fiberax can right-size your Nvidia GPU footprint to budget and roadmap.