H100 PCIe on io.net
for LLM Inference

Is the H100 PCIe on io.net good for LLM Inference?

Excellent fit. AIMC's fit score combines VRAM headroom, GPU class match, and FP16 compute against the workload's requirements — independent of pricing.

• Datacenter class is well-suited for LLM Inference
• 80 GB VRAM provides ample headroom (6.7x the minimum)
• 756 FP16 TFLOPS substantially exceeds the 50 TFLOPS threshold

What this costs on io.net

Listing-weighted median across 2 observed H100 PCIe listings at io.net. The same GPU is tracked at 8 marketplaces total.

Same H100 PCIe at other marketplaces

Top 5 alternative providers for the same GPU, sorted by price ascending.

About LLM Inference

Serving large language models for chat, completion, and agentic workloads. LLM Inference requires at least 12 GB VRAM and benefits from Datacenter or Workstation or Consumer-class compute.