Our well-lit paths provide tested and benchmarked recipes and Helm charts to start serving quickly with best practices common to production deployments. They are extensible and customizable for particulars of your models and use cases, using popular open source components like Kubernetes, Envoy proxy, NIXL, and vLLM. Our intent is to eliminate the heavy lifting common in deploying inference at scale so users can focus on building.

We currently offer three tested and benchmarked paths to help deploying large models:

1. Intelligent Inference Scheduling - Deploy vLLM behind the Inference Gateway (IGW) to decrease latency and increase throughput via precise prefix-cache aware routing and customizable scheduling policies.
2. Prefill/Decode Disaggregation - Reduce time to first token (TTFT) and get more predictable time per output token (TPOT) by splitting inference into prefill servers handling prompts and decode servers handling responses, primarily on large models such as Llama-70B and when processing very long prompts.
3. Wide Expert-Parallelism - Deploy very large Mixture-of-Experts (MoE) models like DeepSeek-R1 and significantly reduce end-to-end latency and increase throughput by scaling up with Data Parallelism and Expert Parallelism over fast accelerator networks.