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Data Center AI Inference GPUs

Data center AI inference GPUs are GPUs specifically optimized for inference workloads. Compared to training cards, they prioritize throughput per watt, latency, and TCO (Total Cost of Ownership). With explosive LLM inference demand in 2025, the inference GPU market is rapidly expanding.

Mainstream AI Inference GPU Comparison​

ModelArchitectureMemoryFP8 ComputeTDPForm FactorBest Use Case
NVIDIA B300 UltraBlackwell Ultra288GB HBM3e14 PFLOPS (FP4 sparse)1,400WSXMDeepSeek 22,476 TGS Prefill
NVIDIA B100/B200Blackwell192GB HBM3e7-9 PFLOPS700-1000WSXMFlagship inference
NVIDIA H200Hopper141GB HBM3e3,958 TFLOPS700WPCIe 5.0Long context inference
NVIDIA H100Hopper80GB HBM33,958 TFLOPS700WPCIe 5.0Large-scale LLM inference
NVIDIA L40SAda Lovelace48GB GDDR6 ECC733 TFLOPS350WPCIe 4.0General inference, Omniverse
NVIDIA L4Ada Lovelace24GB GDDR6485 TFLOPS72WPCIe 4.0Cloud inference, video AI
NVIDIA L2Ada Lovelace24GB GDDR696 TFLOPS50-75WPCIe 4.0Edge / telecom inference
NVIDIA A100 80GBAmpere80GB HBM2e624 TOPS (INT8)400WPCIe 4.0Large model inference
NVIDIA T4Turing16GB GDDR6N/A70WPCIe 3.0Light inference, vGPU

Selection Guide​

By LLM Scale​

  • >70B parameter LLM: H100/H200 (multi-card) / A100 80GB (multi-card)
  • 30B-70B parameter LLM: A100 80GB single / L40S
  • 7B-30B parameter LLM: L40S / L2 / RTX 6000 Ada
  • <7B parameter LLM: L4 / T4 / L2

By Power / Density​

  • Extreme low power (cloud-native): L4 (72W) / L2 (50-75W)
  • Low power (vGPU): T4 (70W)
  • Medium (general): L40S (350W)
  • High performance: H100 / H200 (700W)

By Workload​

  • Generative AI inference (LLM): H100 / H200 / L40S
  • Cloud gaming / video transcoding: L4 (AV1) / T4
  • Batch inference / recommendation: L4 / L2
  • Ultra-low latency: Groq LPU (non-GPU)

Detailed Product Pages​