Lyceum vs Wafer

API access to open-source models with pay-per-token pricing.

Reserved GPU capacity for production models ensuring low latency and high availability.

Run training jobs on GPUs without the need for infrastructure management.

Full root access to customizable GPU instances ready in seconds.

Support for configurations from 8 to 8,000 GPUs with InfiniBand connectivity.

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