The challenge
Understand how local inference workloads interact with hardware constraints while keeping tool-enabled workflows conscious of their access and permission boundaries.
Context and scope
Personal research and development with local inference and tool-enabled workflows, separate from professional work. This is not commercial AI delivery experience.
My responsibilities
- Explored local LLM execution using llama.cpp and Ollama.
- Worked with multi-GPU and long-context workloads under resource constraints.
- Explored security-conscious MCP integrations and agent workflows.
Engineering approach
- Considered model execution, context requirements, and available resources together.
- Explored multi-GPU configurations without assuming that additional hardware removes workload constraints.
- Examined tool access and agent permissions as a distinct concern from model inference.
Outcomes
- Gained hands-on experience with local inference tooling and resource-constrained workloads.
- Developed hands-on understanding of MCP tool access and agent permission boundaries through personal experiments.
What this work illustrates
Running a model locally does not eliminate resource limits or make tool access safe by default. Inference capacity and agent permissions are separate engineering concerns.