Last week I finally did the thing every founder promises they will "get to soon": I ran a serious open-weight model on my own GPU and pushed it through our real workflows. Zhipu's GLM-4.6 slipped out quietly, but it delivered something I have waited years to write. For the first time, a local model handled operator chores without breaking a sweat, without cooking my laptop, and without hallucinating its way through simple prompts.
The setup that made it click
Our GPT-4 bill hovered around 200 to 250 dollars a month for doc summaries, spreadsheet checks, and copy cleanups. Nothing glamorous, just a steady drain. The GLM-4.6 benchmarks promised GPT-4 level coding for free, so I cleared a Saturday. Here is the stack that worked:
- Mac mini with Apple silicon plus an external RTX 4070 (8 GB VRAM).
- Ollama as the orchestration layer with the GLM-4.6 Q4_K_M quantized build.
- Prompts copied straight from the agents we already run in production.
First runs were slow, but once the caches warmed up the model responded 30 to 40 percent faster than GPT-4 on similar jobs. It handled 200K context windows, matched GPT-4 Turbo on structured writing, and beat it on code snippets. "Production ready" is a bold phrase, but GLM-4.6 earned it.
What changed for the team
By Monday, our infrastructure plan flipped. Summaries, quick copy rewrites, and basic code reviews now run locally at zero incremental cost. The 500 dollars spent on the GPU will pay for itself inside a quarter. More important, chaining tasks finally feels instant because we skip the round trip to Azure or OpenAI. No per seat licenses, no quota anxiety, and no panic when the team hits monthly token caps.
How to try it yourself
If your crew spends more than 200 dollars a month on GPT-4 for day to day workflows, stop reading reviews and run the experiment yourself. Use the exact scripts you rely on at work and give it one week. Here is the checklist I used:
- Install Ollama (one command).
- Pull the GLM-4.6 Q4_K_M build.
- Run your real agent scripts, not toy prompts.
- Track time saved, quality, and any regressions by Friday.
If it clicks, you gain redundancy, bargaining power for the next API negotiation, and a stress free fallback when cloud bills spike. If it misses the mark, you spent a Saturday mapping the edge cases. That is a good trade.
Gut check
Local AI is no longer a science project. Memory spikes still happen and fine tuning is not plug and play, but ordinary business workflows finally have open-weight models that keep up with the best SaaS options. The question is not "is GLM-4.6 smarter than GPT-4?" The question is "do I need to pay for GPT-4 on every task?"
This week the answer was no, and I have waited a long time to type that sentence. Forward this to the teammate who watches the cloud bill. Local AI is ready when you are.
