I help deploy and optimize open-source AI and LLM workloads for local and self-hosted environments, with a focus on inference performance, memory constraints, and hardware efficiency.

This is particularly useful when privacy, infrastructure cost, latency, offline operation, or hardware limitations make cloud-only inference impractical.

• Local and self-hosted LLM deployment • Model selection for available hardware • GPU and VRAM utilization analysis • Inference optimization • Quantization and memory optimization • KV-cache and context-memory analysis • Resource and performance profiling • Benchmarking across hardware configurations • Local AI runtime architecture • Deployment and integration of open-source models

I can work with constrained consumer hardware as well as higher-end GPU infrastructure, depending on the workload and model requirements.

The objective is not simply to make a model run, but to understand the hardware and workload constraints and engineer a deployment that makes effective use of the available resources.

Best suited for: teams experimenting with local AI, companies evaluating self-hosted inference, developers running open-source models, and organizations looking to reduce reliance on external AI APIs.