VMware’s private cloud has more to do. Here’s what changed.
VMware Explore 2026 and VCF 9.1.1, explained: private AI, agent controls, platform operations, and what is available now.
Running a model privately is a systems decision. The weights must fit somewhere, tokens must arrive quickly enough to be useful, and the cost has to include idle hardware as well as peak throughput. These posts record what I ran and what the measurements did—and did not—support.
Begin with the local-model experiment, then follow the memory-bandwidth limit into the larger capacity question. The rental and breakeven articles compare ways to get compute without pretending that theoretical throughput is sustained utilization. Prices and versions belong to the date of each experiment.
Start here
- Meta's Muse Glimmer runs on my laptop with the Wi-Fi off
- I tried to make my laptop AI twice as fast. I found the wall instead.
- A 2.8-trillion-parameter model fits in 8 GB of RAM. It writes one token every 32 seconds.
- The GPU I need does not live in my house. So I rent someone else's and pretend it does.
- Self-hosting inference beats the API at 21 billion tokens a month. That is 8,000 tokens per second, forever.
- I wanted private AI. What I had to decide was Kubernetes.
Field notes on this topic
The GPU I need does not live in my house. So I rent someone else's and pretend it does.
My homelab is great at everything except the one thing AI needs: a real GPU. So I rent one by the hour from a neo-cloud, pull it into the tailnet, and tear it down in a finally block. The buy-versus-rent math, priced honestly.
Self-hosting inference beats the API at 21 billion tokens a month. That is 8,000 tokens per second, forever.
Working out when self-hosting an open model actually beats a managed inference API, using published list prices. The breakeven is not a price per token, it is a sustained throughput you have to hit every second of every day.
I wanted private AI. What I had to decide was Kubernetes.
The GPU is the part everyone budgets for and the part I never needed. An open model runs privately on CPUs you already own — so the real decision is the platform underneath it.
I tried to make my laptop AI twice as fast. I found the wall instead.
A build log on speculative decoding, memory bandwidth, and why the honest answer to 'can we make it faster?' was 'not today' — plus exactly why, and why that's still a good day's work.
Meta's Muse Glimmer runs on my laptop with the Wi-Fi off
I put Meta's new open-weight Muse Glimmer 30B on my MacBook, pulled the Wi-Fi, and it kept working. A build log on local AI — and why running it yourself is an architecture decision, not a hobby.
A 2.8-trillion-parameter model fits in 8 GB of RAM. It writes one token every 32 seconds.
Two new engines run frontier Mixture-of-Experts models on consumer hardware by paging experts off SSD instead of holding them in memory. Why that works, why it is slow, why C is not the reason, what a GPU does and does not fix, and what it means for air-gapped estates and hardware refresh planning.
There is no cloud. It's a Dell workstation in a spare room.
A mid-2026 state-of-the-homelab: one Dell workstation running Proxmox, a Raspberry Pi, a bare-metal ESXi box, and twelve Tailscale devices running real websites and a fleet of agents, with no public IP and one deliberate single point of failure.
I didn't give my agents a vector database. I gave them 126 text files.
A working design for agent memory that survives the restart: one fact per Markdown file, an index loaded every session, wiki-links instead of a graph database, and the ways it quietly rots. Measured from a real store of 126 memories.
I built a machine that wrote 278 articles. Nobody read them.
Apollo published 278 articles in 14 weeks to zero subscribers. A postmortem of a pipeline that worked as software and failed as a publication.