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GPU capacity for AI inference serving

Inference is an operations problem: engine selection, KV cache behaviour, batching and failover decide your cost per token far more than the GPU model does. We run all of it.

OpenAI-compatible

API

vLLM / SGLang / TRT-LLM

Engines

Per token

Metering

Multi-node

Routing

The corridor between two server rows, cable trunking running overhead

Serving capacity behind an OpenAI-compatible endpoint

  • An OpenAI-compatible endpoint your existing SDKs and agent frameworks talk to unchanged

  • Engine selection and parameter tuning per model, not default flags copied from a README

  • Extended KV cache (LMCache, HiCache) to cut time-to-first-token on long contexts

  • Speculative decoding where the model supports it: EAGLE3, MTP or n-gram drafting

  • Request-level metering, rate limiting and Prometheus metrics you can audit

Before you ask

Is the inference API compatible with the OpenAI SDK?
Yes. Endpoints implement the OpenAI chat completions surface, so the official SDKs, LangChain, LlamaIndex and most agent harnesses work without code changes. Our public router, GonkaRouter, runs on the same principle.
Which inference engine will you run for my model?
Whichever benchmarks best for your traffic shape. LLMFabric, our open-source cluster manager, automates engine selection and parameter tuning across vLLM, SGLang and TensorRT-LLM, and you can plug in a custom engine.
How do you reduce time-to-first-token on long-context workloads?
Through extended KV cache systems (LMCache, HiCache) that keep prefix state warm across requests, plus speculative decoding methods such as EAGLE3, MTP and n-gram drafting where the model supports them.

Other workloads