[bmdpat]

/* ------------------------------------------------ */

BMD

// build. measure. deploy.

*/

pat@5090: ~/desk

A real shell running in your browser. Type desk to size your GPU, or tap a command. The bench replay streams at the measured rate. It is a replay, not a live model.

pat@5090:~$ cat latest_sweep.md

THE 5090 REPORTS // the lab notes behind the desk

I run open models on my own GPUs and publish the results.

Each report shows the model, quant, prompt, hardware, speed, VRAM use, and what broke. AgentGuard handles spend limits, loops, timeouts, and rate limits.

Read the 5090 Reports

pat@5090:~$ column -t fig1_sweep.tsv

Headline run01 / 06
Model
llama3.1:8b
Quant
Q4_K_M
Generation
228.9 TOK/S
Peak VRAM
7.2 GB
Open all six measured rows
Fig. 1 Six runs on the RTX 5090, Ollama 0.31.1, temperature 0. llama3.1:8b Q4_K_M reached 228.9 tok/s generation, measured 2026-07-09.Raw artifact

pat@5090:~$ tail -n 1 stderr.log

[WARN] stderr

Changing num_ctx between requests forces a full model reload. On a 26B that is 140 seconds per swap. Pin your context size.

Field note, 2026-07-09

pat@5090:~$ agentguard --status

I use AgentGuard to stop spend, loops, timeouts, and rate-limit failures before a test touches real work.

$ pip install agentguard47
Open AgentGuard docsRead the 5090 Reports

pat@5090:~$ ls tools/

§ 003 / TOOLS

Local AI tools + 12 live tools.

12 live / 1 beta from 13 public tools. Open what helps.

01 / free+pro / browser* LIVE
VRAM Calculatorweb
02 / free+pro / browser* LIVE
Model Pickerweb
03 / free+pro / browser* LIVE
Quantization Compareweb
04 / $7/mo* LIVE
Local LLM Toolkit Pro7-day trial
05 / free / Python* LIVE
AgentGuardv1.2.13
$ pip install agentguard47
06 / free / browser* LIVE
Agent Roadmap Scannerweb

pat@5090:~$ ps aux | grep agents

§ 004 / OPERATING LOOP

One person. Small tools. Agent-assisted ops.

01

Run

Run the model on the target GPU.

02

Measure

Record tokens, latency, VRAM, cost, and failure mode.

03

Publish

Write up the result or ship the tool it required.

pat@5090:~$ tail blog.log

§ 005 / BUILD NOTES[ ALL POSTS ] ->

Build notes.

01
5 min read

The faster local model run took 83x longer

02
6 min read

Chunk Size Is a Reliability Setting

03
5 min read

Why Local LLM Benchmarks Need Power Data

04
5 min read

VRAM Fit Is Not Runtime Support

pat@5090:~$ mail -s "subscribe" pat@5090

To: the journal // Subj: subscribe

Get the journal by email.

New benchmark results, failed runs, VRAM checks, and tools. I send an email only when there is a real result.

Get the Local AI Field Kit

Four copy-ready tools now, then measured local AI field notes M-F only when there is something worth sending.

Free. One-click unsubscribe. No sponsored placements. Your email is used only for these notes.

[bmdpat] 0:desk*VRAM 7.8/32.0 GB