Compare Two GPUs Side-by-Side — H100 vs A100, RTX 4090 vs RTX 5090 & More

Each GPU vs GPU comparison page shows a full side-by-side breakdown of architecture, VRAM, memory bandwidth, FP16 TFLOPS, TDP, NVLink support, and release year. Live cloud pricing from 94+ providers is pulled in real time so you can see the current on-demand and spot cost for each GPU alongside its raw specs. A winner is highlighted for each metric row, and the page includes a use-case recommendation — training, inference, or image generation — based on the GPU pair's relative strengths.

H100 vs A100 — the most compared pair

The NVIDIA H100 vs A100 comparison is the most searched GPU pair for LLM training decisions in 2026. The H100 SXM delivers 3.35 PFLOPS FP16 vs the A100's 0.77 PFLOPS — a 4.3× raw compute advantage — but costs 2–3× more per hour on-demand. For large-scale pre-training runs (70B+ parameter models), the H100's NVLink 4.0 interconnect (900 GB/s) and HBM3 memory bandwidth (3.35 TB/s) make it the clear choice. For fine-tuning and inference on smaller models, the A100 80GB often delivers better cost-per-token due to its lower hourly rate.

RTX 4090 vs data-centre GPUs

The RTX 4090 is a consumer GPU that has become popular for cost-sensitive AI workloads due to its low spot price ($0.12–$0.18/hr on Vast.ai) and 24GB GDDR6X VRAM. It delivers 82.6 TFLOPS FP16 — comparable to the A10G — making it viable for inference on 7B–13B parameter models and fine-tuning with QLoRA. Its main limitations are the lack of ECC memory, no NVLink support, and lower memory bandwidth (1 TB/s vs 2 TB/s for A100) which bottlenecks attention layers in larger models. Compare RTX 4090 vs L40S for image generation workloads, or RTX 4090 vs A100 for inference cost analysis.

Hopper vs Ampere architecture differences

NVIDIA's Hopper architecture (H100, H200) introduced three major improvements over Ampere (A100, A40, A10): the Transformer Engine with FP8 mixed-precision training (2× throughput vs FP16 on Ampere), NVLink 4.0 with 900 GB/s bidirectional bandwidth (vs 600 GB/s NVLink 3.0 on A100), and HBM3 memory with 3.35 TB/s bandwidth (vs 2 TB/s HBM2e on A100 80GB). For LLM training, the Transformer Engine's FP8 support is the most impactful — it reduces memory footprint and increases compute utilisation on attention and feed-forward layers. For inference, the higher memory bandwidth directly reduces time-to-first-token for large models.

When to use spot vs on-demand GPU instances

Spot (preemptible) GPU instances are 40–70% cheaper than on-demand but can be interrupted with 30 seconds to 5 minutes notice depending on provider. They are appropriate for fault-tolerant training jobs with checkpointing, batch inference pipelines, and development workloads. On-demand instances are required for production inference serving, real-time applications, and any workload where interruption would cause user-facing downtime. H100 spot prices currently range from $1.60–$2.50/hr vs $3.50–$5.50/hr on-demand — a saving of $14,000–$26,000/month for a single H100 running continuously.