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GPU Performance At A Glance

It is a single relative number showing how a GPU performs across LLM inference, image generation and vision workloads combined, scaled so the RTX 3090 always equals 100. A score of 200 means roughly twice the aggregate throughput of an RTX 3090.

The RTX Pro 6000 Blackwell currently leads our ranking with an AI Inference Index of 403 — roughly four times the aggregate inference throughput of the RTX 3090 baseline — paired with 96 GB of VRAM for the largest LLMs without quantization.

The RTX 5090 (AI Inference Index 207, 32 GB) and RTX 4090 (index 133, 24 GB) offer the strongest price-to-performance for single-GPU inference and image generation. The RTX 4090 Pro variant adds 48 GB of VRAM for larger models while keeping consumer-class value.

For the largest LLMs served without quantization, flagship data-center cards like the RTX Pro 6000 Blackwell (96 GB), H200 (141 GB) and Instinct MI300X (192 GB) give you the memory headroom to keep whole models resident. For 8B to 30B models, a single RTX 5090 or RTX 4090 delivers excellent throughput at a fraction of the cost.

Yes — current-generation NVIDIA GPUs and full AI servers are available through our AI Hardware catalog, with worldwide DDP shipping and B2B pricing on request.

It is one of the most widely deployed AI-capable GPUs, making it a practical, well-understood reference point for comparing newer and older hardware.

No. VRAM determines which model sizes and batch sizes fit at all — it does not by itself determine speed. A card with less VRAM can still post a higher inference index if its architecture and memory bandwidth are stronger.

The AMD Instinct MI350X leads at 288 GB, followed by the MI325X at 256 GB. Among NVIDIA cards the B100 SXM offers 192 GB and the H200 provides 141 GB of high-bandwidth memory. More VRAM lets you load bigger models and larger batches without splitting them across multiple GPUs.

FP16 TFLOPS is a theoretical peak compute figure published by the manufacturer — a raw hardware ceiling. The AI Inference Index is a measured, workload-based score built from published benchmarks across LLM, image and vision tasks. A card can show high theoretical TFLOPS yet a lower real-world index when memory bandwidth or software support holds it back.

Both are Hopper-architecture data-center GPUs rated at 989.5 FP16 TFLOPS. The difference is memory: the H200 carries 141 GB of faster HBM3e versus 80 GB on the H100, so it serves larger models and longer context windows at higher sustained throughput. For most new LLM-serving deployments the H200 is the stronger choice.

Yes. Use the comparison Arena on this page to pick any two of our 78 listed GPUs and see their AI Inference Index, VRAM, FP16 TFLOPS and training throughput side by side, with the winner of each metric highlighted.

Training is far more memory- and compute-intensive than inference, so it favors flagship cards with large VRAM and high FP16 TFLOPS such as the H100, H200, B200 or Instinct MI300X class. Our Training Throughput column shows single-GPU images per second where a published benchmark exists — for example 1,396 img/s on the A100 40GB versus 905 on the RTX 3090.

Yes. Alongside 78 individual GPUs we list 48 complete multi-GPU AI servers, including HGX and DGX-class systems built around H100, H200 and Blackwell accelerators, ready for large-scale training and production inference. Contact us for configuration and B2B pricing.

All three are covered. Alongside the full NVIDIA line-up we include AMD Instinct accelerators (MI350X, MI325X, MI300X, MI250X) and Radeon cards, plus Intel Gaudi 3 and Arc, so you can compare across vendors on one consistent index.

Every figure is drawn from publicly published benchmark and manufacturer data, aggregated into a consistent index for orientation. Real-world results vary with software stack, drivers, batch size and workload, so treat the numbers as a comparative guide rather than a guarantee of performance.

We review and refresh figures as new GPU generations launch and as more benchmark data becomes available.