IO.NET vs Wafer

Access to 300,000+ verified GPUs from 139 countries with 6,000+ cluster-ready GPUs

Deploy clusters in under 90 seconds with auto-scaling capabilities

Choose from containers, Ray clusters, or bare metal based on workload needs

Uses the same distributed computing framework that OpenAI used to train GPT-3

AI models, smart agents, and API integration for workflow automation

Kernel-level VPN with secure mesh protocols for data protection

Pay-as-you-go API access to hosted open-source models including GLM, Kimi, Qwen, and DeepSeek with no infrastructure management

Custom-tuned inference deployments with performance guarantees, provisioned in under 24 hours

Agents profile inference bottlenecks and tune across serving engines (vLLM, SGLang, TensorRT-LLM), custom kernels (CUDA, HIP, Triton, NKI), quantization (FP8/FP4), and decode strategies

Workloads run on NVIDIA B200/B300, AMD MI350X/MI355X, and AWS Trainium depending on the model and traffic shape

OpenAI-compatible endpoint at pass.wafer.ai/v1 and Anthropic-compatible endpoint at pass.wafer.ai/v1/messages, both using Bearer token authentication

Cached input tokens are billed at reduced rates on supported models