SEC 001 / LOCAL HARDWARE
I use an RTX 5090 to test local models, agent runs, and workflow experiments. The notes track what fits in VRAM, what breaks, and when local hardware is worth the extra setup.
AgentGuard stays in the stack when a run needs budget, loop, timeout, or rate limits before it touches real work.
The first public run proved RTX 5090 hardware detection and exposed an Ollama runner timeout before a valid tokens/sec row. That failure stays in the notebook.
Every public claim needs a concrete artifact: benchmark CSV, failure report, architecture diagram, repo note, or cost curve. If a run fails, it stays in the record.
Measured on real local-agent prompts, not placeholder demos.
What fits, what spills, and what changes after quantization.
Per-workload math for agents that run often enough to matter.
Runner crashes, bad configs, and dead ends stay in the record.
Hardware capture is live. The first bounded Ollama benchmark failed before a valid tokens/sec row, so the public artifact reports the miss instead of inventing a performance claim.
Source: nvidia-smi + Reports/5090/benchmarks
The Ollama request timed out after 5 seconds with gemma4:26b at num_ctx 1024 and num_predict 16.
Source: Reports/5090/failures/2026-06-12-gemma4-26b.md
The destination is self-serve local-agent infrastructure: observability, memory, MCP, runtime limits, and local hardware fit.
Phase 0
Weekly reports from hardware snapshots, benchmark CSVs, and failure logs.
Phase 1
Three posts per week across LinkedIn, X, and r/LocalLLaMA, all pointing here.
Phase 2
Inbound-only, async paid R&D for regulated teams that need local AI.
Phase 3
Local agent observability, memory, or MCP tooling rebuilt from repeated deployment work.
Publish the model, quant, prompt, hardware, and result.
Run one local experiment per week, even when the result is a failure.
No fake benchmark numbers. A failed run is a valid artifact.
Prefer measured notes over broad claims.
12 live from 12 public tools. Open what helps.
$ pip install agentguard47$ pip install agentguard47Execute the local model path on local GPUs.
Record tokens, latency, VRAM, cost, and failure mode.
Turn the result into a report, tool, or guarded SDK path.
Weekly notes from the 5090 lab: benchmark rows, failure logs, private-agent deployment notes, and the tools that fall out of repeated local AI work.
AI agent builds, real costs, what works. M-F only when there is something worth sending. No fluff.
Weekly local-GPU notes from scheduled lab runs on an RTX 5090: benchmark artifacts, model-fit checks, and failure logs.