"The GLM version of Strata": how one person got 31 days and 2,500 stars, and how Project Maya slipped into a hit-product gap

A Japanese blogger, September, installed Strata, the most popular local inference engine, at the end of the month and wrote at the bottom of his post, "GLM-5.3-Flash doesn't run on Strata, so I'll leave it for now." Ten days later he found that someone had already done it. Project Maya got a 321B GLM-5.3-Flash running on one consumer GPU plus RAM and an SSD, and earned 2,549 stars in 31 days. The companion quantized model was downloaded over a million times on Hugging Face. Below: why it took off, where the stars came from, how much room is left, and how to use this playbook if you want a project that gets similar stars.

One "leave it for now" line, and ten days later someone finished it

In Oct 2026 10, Japanese blogger zephel01 posted an install log on note.com, opening like this:

"10 days ago, at the end of my Strata install log, I wrote 'GLM-5.3-Flash doesn't run on Strata, so I'll leave it for now.' The architecture is different, so it would have to be rewritten and ported from the kernel up, that's how I estimated it. The port is out now. It's called Project Maya."

When he installed it, the repo was only three days old and already had 144 commits and had shipped v1.0.23. His machine had an RTX 5090 (32GB) plus 94GB of RAM. The whole install took 45 minutes (most of it downloading the 95GB model). He asked "What's the capital of Japan?", got "Tokyo", at 33 tokens per second. The built-in benchmark averaged 34.9 tokens per second.

At about the same time, the same post appeared on r/LocalLLaMA and r/LocalLLM, its title directly borrowing the upstream name: "'Strata' for GLM5.3 Flash is here".

What it is

An open-source local inference engine that squeezes several large MoE models that "normally need a data center" onto an ordinary computer: GLM-5.3-Flash (321B), and later DeepSeek-V4.1-Flash (748B) and MiniMax-M3.1-Flash (428B).

The approach copies Strata, and the README spells it out: these models use only a small set of "experts" per token, so the most-used experts stay in VRAM, the next most-used in RAM, and the rest on an NVMe SSD, shuffled as the conversation goes. On install it probes your GPU, CPU, RAM and SSD, then tunes its own parameters. zephel01's GPU ran on only x4 PCIe, and the engine measured "shipping experts to the GPU takes 1.04 ms, computing them on the CPU takes 0.09 ms", so it just let the CPU do the work. Details like this are why it runs on all kinds of mismatched hardware.

Wrapped around it: a browser chat and monitoring dashboard, one-click model download and switching, OpenAI- and Anthropic-compatible APIs (so coding agents can plug in, and later Codex CLI too), and a double-clickable START-MAYA.bat on Windows. Licensed MIT. The author is Peasant Smith (on GitHub as mw00), with a co-maintainer, needmorevram, building the web UI.

Why now

Three things stacked together:

• The model was hot: GLM-5.3-Flash launched in Aug 26, scored 1,132 points on HN, and was among the best-regarded open coding models at the time. But on local hardware, 321B ran at only 1 to 10 tok/s with general-purpose setups. Reddit's verdict was "roughly 10 tok/s, basically unusable."
• Upstream proved the demand but didn't add support: Strata launched in Sep 24, grew to nearly 20k stars in two weeks (now over 40k), and showed that a large crowd wants "gaming GPU plus SSD runs big MoE models." But it mainly targets Qwen3.8-Flash-Next, and the README has no GLM.
• Maya was created in Oct 6, and it filled exactly that gap. Going from 10 tok/s to 30 tok/s is the most immediately visible selling point in that gap.

Where the stars came from

(GitHub daily stars; 361 forks, fork rate 14%, above the ~11% typical for new repos of similar size)

A few channels, all organic. I didn't see any paid spend:

• Borrows the upstream's name. The post title is "Strata for GLM", and the README openly says it was split from Strata. Anyone who knows Strata gets what this is at a glance.
• Reddit's local-LLM crowd. Launched on r/LocalLLaMA and r/LocalLLM, with later discussion threads in communities like r/StrixHalo (AMD setups).
• The author posts on X for nearly every release. @PeasantSmith has over four thousand tweets, and the bio reads "Creator of Project Maya · 1M+ downloads on HF". In Oct 25 they shared a "410k downloads" milestone, and certain accounts regularly repost each update. In 31 days there were 616 commits, with version numbers up to v1.1.55, so every release is itself a promotion.
• Reverse traffic from Hugging Face. The GitHub profile links to the author's Hugging Face page, and every Maya quantized model card says "made for Project Maya" and links back. The GLM quant has 1.06M downloads and the DeepSeek quant 200k (Hugging Face, 11-06; counted per file, and one release is split into dozens of files, so this isn't 1.06M users, but it does mean lots of people are pulling tens or hundreds of GB). Someone searching "GLM-5.3-Flash GGUF" can follow the model card to the engine.
• Turn user benchmarks into content. --bench --report sends results as an issue in one command. The README now lists speeds from about 90 real machines, from 8 to 5090, including older P40 cards, mining cards, AMD MI50, and Strix Halo, with each entry linked to its issue number. Anyone sitting on an old GPU can see right away whether their machine will run it.

It has not hit the HN front page and has had no major media coverage.

Why it holds up, and why it's cooling off

On the same day (Oct 6) another Strata fork, Xolotl, appeared. It focused on DeepSeek, later added GLM, and now has 467 stars, about a fifth of Maya's. The approach is almost identical. The difference is that Maya added a few things that build trust:

• Its own quants, with quality disclosed. Each quant states how much zero-shot accuracy it keeps relative to the original FP8 (97.7% to 99.2% for the GLM version), which addresses the worry that low-bit quants break the model.
• Very broad hardware support. Older V100, P40, mining cards, AMD ROCm and Windows all run, which speaks directly to the most active part of r/LocalLLaMA.
• One-click install, fast fixes. Bug reports and benchmark submissions quickly make it into the next release.

But much of its differentiation is a head start. Strata has since added DeepSeek itself, Xolotl added GLM, PolyStrata packs a dozen-plus models into one program (800 more stars), and smaller ones like Infernix (268 stars) and dsv41-flash-offload (155 stars) have appeared. The exclusive model is gone, and weekly growth fell from +735 to +355. What keeps it going is the batch of quantized models on Hugging Face and the reputation built by community benchmarks.

The "single-model engine" has become a set pattern

In Oct 11, someone opened an issue on the Strata repo: "when support kimi k3" (when will Kimi K3 be supported). It was tagged "not planned" and closed. The reply underneath said: "fork it and do it yourself. Single-model engines are the newest thing."

That reply sums up Maya's approach. The upstream hit only supports one model. Each time a new large MoE comes out, there's room for an engine rewritten for that one model plus a matching set of quants. Hugging Face already has people tacking suffixes like "-Strata-GGUF" and "-agentic-strata" onto model names. The problem is that everyone can see this opening, and in the month-plus since Strata, five or six forks have already crowded in.

If it were you

Verdict: B. The playbook is clear and can be copied, but the models that are available have already been claimed. The next opening depends on a new model release, and you need to be genuinely able to write inference kernels.

Who it suits: people who have written CUDA/ROCm kernels, know llama.cpp or GGUF quantization well, have one or two cards to test on, and want to build a reputation and following around an open-source project.

Who it doesn't suit: people who only call APIs and wrap a UI around them. This crowd benchmarks on their own machines and posts numbers in issues. If speed is poor, nobody talks about it within a week. Building another "Strata for GLM / DeepSeek" is already too late.

How to do it, ranked by how worthwhile:

1. Watch for release dates of the next large open MoE. Pick a model in the 300B to 800B parameter range that gets good buzz after release but that Strata and its forks haven't covered yet, and ship a one-click engine rewritten for it within one to two weeks of release. Kimi K3.1 Pro released its weights in early November, but it has 2.8T parameters, and even the smallest quant far exceeds consumer-machine memory, so it isn't this opening. You want the next one that just fits in 5090 plus 128GB of RAM.
2. Publish your own quants to Hugging Face at the same time, with model cards stating "built for [engine]", disclosing quality retention relative to the original, and linking both ways. Maya's Hugging Face downloads are over four hundred times its GitHub stars (counted per file), making that its biggest second entry point and a milestone worth sharing.
3. Ship "one command to submit benchmarks" from day one, with a README listing real speeds by machine. This works better than any marketing, and it lets users contribute on their own.
4. If you can't write kernels, step back to the periphery: a Windows/AMD one-click package for an existing engine, a benchmark aggregation site, or config templates for coding agents. The star ceiling is lower, but you aren't betting on a model.

How to get users: put the upstream's name in the title ("X for [model]"), state openly in the README where it was forked from and be polite to the upstream. Launch on r/LocalLLaMA and r/LocalLLM, and find Japanese note and zenn bloggers and dev.to writers to do install logs (this crowd loves writing them). Post on X with each release, and share a post at 10k and 100k downloads.

Timing: within two weeks of a new model's release. Maya took 41 days from GLM's release to repo creation, but its real window was the 12 day after Strata took off. Any later and the upstream or another fork fills the gap.

Signs you've misjudged it: ① Strata becomes plugin-based and adds all the major large MoEs within a month, so the single-model slot disappears. ② llama.cpp adds SSD tiering and auto-tuning, and a general-purpose one-click option catches up on speed, making this whole category a stopgap. ③ Conversely, if Maya stays above +30 per day into late November and Hugging Face downloads keep rising, that shows early movers can hold their position through quant assets and reputation, and followers should pick a different model rather than the same one.

Data and sources

GitHub: mw00/project-maya repo page, README, commit history and daily stars (10-17 to 11-06); similar repos such as Strata and Xolotl; Strata issue #1954 (10-11). Hugging Face: download counts for models under peasantsmith (11-06). note.com zephel01 install log (10-10). Reddit r/LocalLLaMA and r/LocalLLM post titles and summaries (post bodies could not be opened). dev.to seppegadeyne tutorial (10-16, 10-21), AlphaSignal. X: @PeasantSmith profile and release posts. z.ai GLM-5.3-Flash release blog (8-26). Where the text says "inferred" or "not verified", those points are inference or unconfirmed.