§ 001 / 5090 REPORTS
The 5090 Reports
Weekly benchmark and build logs for local AI agent experiments on controlled compute, starting with an RTX 5090 workstation. The hook is the 5090. The moat is the operating system around local, private, production agents.
§ 002 / CURRENT RUN
The report includes misses.
The 2026-07-09 sweep measured six real tokens/sec rows on the RTX 5090: llama3.1:8b Q4_K_M at 207-229 tok/s and gemma4:26b Q4_K_M at 180-207 tok/s, plus a 140-second model-reload gotcha when num_ctx changes between requests. The June 12 Ollama runner timeout stays in the notebook as the first entry.
Tokens/sec
Model x quant
Measured on real local-agent prompts, not placeholder demos.
VRAM pressure
Context + cache
What fits, what spills, and what changes after quantization.
Cost curve
Local vs API
Per-workload math for agents that run often enough to matter.
Failure log
Timeouts included
Runner crashes, bad configs, and dead ends stay in the record.
Latest public artifact
The 5090 Reports - 2026-07-09
Six measured tokens/sec rows on the RTX 5090: llama3.1:8b Q4_K_M holds 207-229 tok/s generation and gemma4:26b Q4_K_M holds 180-207 tok/s, with VRAM, watts, and a 140-second model-reload gotcha documented in the notes.
- GPU
- NVIDIA GeForce RTX 5090
- Captured from nvidia-smi on the primary dev machine.
- Driver
- 610.62
- Driver reported by the 2026-07-09 hardware snapshot.
- Memory
- 20233 / 32607 MiB peak
- Peak memory during the gemma4:26b Q4_K_M benchmark runs.
- Power
- 463 / 575.00 W peak
- Peak power draw during generation and the board power limit.
Distribution kit
Three drafts from the same artifact.
LinkedIn, X, and r/LocalLLaMA drafts use the latest raw report, the failure log, and the email-list CTA.
- Lab note
- One artifact, one current result, one email-list CTA.
- X
- Thread
- Short claims that point to the raw report instead of screenshots.
- r/LocalLLaMA
- Build log
- Failure-first technical context for local-inference builders.
AgentGuard install path
Guard the run before it becomes a system.
The 5090 Reports measure local agent experiments on controlled compute. AgentGuard is the installable runtime stop layer for the runs that can spend, loop, timeout, or hammer tools.
§ 003 / OPERATING RULES
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.
§ 004 / PRODUCT PATH
Content is the sensor. Product is the output.
The loop is simple: benchmark in public, grow the list, take capped inbound deployment work only when it teaches the product, then ship the repeated tooling as self-serve software.
Phase 0
Instrument the lab
Weekly reports from hardware snapshots, benchmark CSVs, and failure logs.
Phase 1
Distribute artifacts
Three posts per week across LinkedIn, X, and r/LocalLLaMA, all pointing here.
Phase 2
Capped deployments
Inbound-only, async paid R&D for regulated teams that need local AI.
Phase 3
Extract product
Local agent observability, memory, or MCP tooling rebuilt from repeated deployment work.
§ 005 / EMAIL LIST
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Benchmark tables, failure logs, architecture diagrams, and repo notes. M-F, only when there is something worth sending. No calls.
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