AI GPU Infrastructure Advisory
AI Workload, Model & Inference Advisory
Right model. Right precision. Right capacity.
AI workload, model and inference advisory starts from what the AI actually has to do — its users, agents and LLM calls — and works out which model to run, at what precision, and on how much GPU capacity.
What we advise
- Workload discovery: users, agents and LLM calls
- Kimi, Qwen and DeepSeek model evaluation
- FP4 / FP8 / BF16 and HBM memory planning
- Input/output tokens per second and latency
- Benchmark-driven GPU sizing
Stop guessing GPU capacity. Start with the workload.
Workload-first / Vendor-neutral / Commercially optimized
Routing requests between models and providers is what prouter.io (opens prouter.io in a new tab) does.
Related solutions
GPU Architecture & Cluster Optimization
GPU architecture choices, NVLink domains, cluster fabrics and scaling, and procurement BOM optimization.
Infrastructure Economics, Capacity & Operations
Commercial models, CapEx, OpEx, TCO and ROI, utilization forecasting and capacity expansion.
Token Economics & Agent Optimization
Agent and concurrency modeling, caching, batching and routing, precision and cost per million tokens.
Next step
Start with the workload.
Tell us what you need to run, where it has to run and what it can cost, and we will set out the options.