Compute.
At your scale.

Hardware. Software. Infrastructure.
Designed around your workload.

Abstract brand studyDrag to rotate

PYROIS FIELD NOTES01 / START WITH THE MODEL

What are
you running?

The workload decides the configuration.

A model that
needs room.

Start with model size, precision and memory requirements. Then evaluate GPU count and multi-GPU scaling.

Define the model, expected traffic and response time. Scope the serving approach around those requirements.

PYROIS GLOBALGPU INFRASTRUCTURE

AMD INSTINCT / ROCm

TRAINING / INFERENCE / DEDICATED SYSTEMSSCROLL TO EXPLORE ↓

DEPLOYMENT NOTE02 / SOFTWARE

Moving from CUDA?

Make the dependency
list first.

Framework

PyTorch, vLLM or SGLang versions

Kernels

Custom CUDA code and library dependencies

Validation

Model quality, throughput and scaling

LET’S DISCUSS THE DETAILSBring your
workload. ↗

GPU Cloud · Token Factory · Private deployments