Local GPU Inference for AI Agents — vLLM, Ollama, Open Weights | aamp

Use cases Local GPU inference

Hardware ROI and open-weight inference

Amplify your local GPU investment from day one.

Native integration for Ollama, vLLM and local open weights, with 0% platform markup.

You bought the NVIDIA cluster or the GPU workstations. Now the team needs a platform that turns raw compute into production workflows. aamp bridges local open-weight models — Llama, DeepSeek, Mistral — with governed agent execution, so you skip SaaS token markups and get predictable return on physical infrastructure.

  • vLLM · Ollama · LocalAI · TGI
  • 0% token markup
  • Llama · DeepSeek · Mistral
  • No egress required
01 · What you get

Why teams run this on aamp.

Your engines, spoken natively

Point aamp at a vLLM, Ollama, LocalAI or TGI endpoint on your own hardware. No gateway, no broker, no per-token platform fee.

Marginal cost of a run approaches zero

Once the hardware is paid for, an agent run costs electricity and time. There is no usage meter between you and your own GPUs.

Mix owned and rented compute

Route the sensitive workloads to local weights and the rest to a frontier API, per agent, under one policy layer.

02 · How it works

Three steps, one perimeter.

01

Serve the weights

Run the model on your GPUs with the engine your team already uses.

02

Register the endpoint

aamp treats a local engine exactly as it treats a hosted API.

03

Assign models per agent

Each agent gets the model its data classification allows.

Illustration to come
Infrastructure · MLOps · Platform engineering · CTO office Same perimeter, same audit trail.

Run it where you can defend it.