Scope of the exploration
This is personal R&D, separate from my professional engineering work and not commercial AI delivery experience. I explore local inference with llama.cpp and Ollama, GPU and multi-GPU execution, and the memory and workload tradeoffs of long contexts. MCP integrations and agent workflows are another focus, with attention to tool permissions and access boundaries.
Problems I work on
- A model can be loaded, but its useful operating envelope changes as context length and hardware resource demands increase.
- Multi-GPU capability introduces additional configuration choices without removing the need to understand memory and workload constraints.
- An agent can call tools, but its allowed actions and access boundaries need more deliberate consideration than ordinary text generation.
How I approach the work
- Treat model choice, context requirements, and available hardware as a combined constraint rather than evaluating the model name in isolation.
- Explore local inference with llama.cpp and Ollama, comparing model configuration, GPU resources, and workload requirements.
- Consider the permissions and access surface of MCP tools and agent workflows explicitly. Local execution alone is not evidence that a workflow is secure.
- Use personal experiments to examine inference constraints and tool behavior before drawing conclusions about a workflow.
Technologies and environments
Frequently asked questions
Is this professional AI delivery experience?
No. This is personal R&D. It demonstrates hands-on exploration of local inference and tool-enabled workflows, not production commercial deployments, client outcomes, or an established AI consulting practice.
What are you investigating with local inference?
How model configuration, GPU memory, multi-GPU execution, and context length affect usable workloads, alongside how MCP permissions shape an agent’s access to tools. These remain personal experiments, not production guarantees.