Question 1

What is LLM Inference & GPU Infrastructure?

Accepted Answer

We design serving stacks that deliver low latency and high throughput while driving down your cost per million tokens — with OpenAI-compatible APIs so your apps don't need to change. We also size, provision and operate the GPU infrastructure underneath — cloud, dedicated or on-prem clusters.

Question 2

What does Lumino deliver as part of LLM Inference & GPU Infrastructure?

Accepted Answer

High-throughput serving engines, AWQ, GPTQ & FP8 quantization, Continuous batching & paged attention, Speculative decoding, Autoscaling, including scale-to-zero, OpenAI-compatible endpoints, Streaming & structured outputs and Cost-per-1M-token optimisation.

Question 3

Which technologies do you use for LLM Inference & GPU Infrastructure?

Accepted Answer

Typical tools include vLLM, TensorRT-LLM, SGLang, TGI, Triton, Ollama, NVIDIA H100, H200, A100 and L40S — we choose the stack after reviewing your data, systems and constraints.

Question 4

What are common use cases for LLM Inference & GPU Infrastructure?

Accepted Answer

Private copilots, High-volume classification, Voice agent back-ends, Training clusters, Inference fleets and Burst capacity for experiments. See our case studies for examples delivered in production.

Question 5

Do you offer LLM Inference & GPU Infrastructure in USA, Canada, Singapore, UAE and India?

Accepted Answer

Yes. Lumino delivers ai infrastructure services to clients in USA, Canada, Singapore, UAE and India — as a fixed-scope project, time & materials, or a dedicated team.