Meet Wally.
Our inference stack for open frontier models, built to be the fastest place to run them.
Performance snapshot:
GLM-5.3 Flash: 380 tok/s.
GLM-5.3 Max: 790 tok/s
Qwen3.8-27B: 485 tok/s
DeepSeek-V4.1 Flash: 615 tok/s
1/7
@AnthropicAI is betting up to 5 GW on @awscloud Trainium. We wanted the latest open models flying on it too but there is ZERO support for those.
So we wrote megakernels and open-sourced TrnRT: Our Tranium inference engine
Running @Zai_org's 320B GLM-5.3-Flash on one Trainium2 box, producing 3.57 M tokens per minute, measured on real coding agent traffic via @SemiAnalysis_'s AgentX.
Trying a new AI chip used to be a lot of work. You often had to rewrite your code, revalidate it, and sink many weeks in, betting this new chip is the right one for your workload.
That cost time and money, and wore on the team. Your best people got buried in plumbing instead of
We tried this on a real restaurant site that was failing Lighthouse. Told DeepSeek V4.1 Flash on Wally to fix it and 6 seconds later it was done, we hadn't even read the first error
Every vibe coder's SEO prompt:
"My vibe-coded site doesn't show up on Google. Fix it.
- Switch to server-side rendering.
- Create an XML sitemap.
- Submit it in Search Console.
- Let Googlebot in through robots.txt.
- Strip out every noindex tag.
- Collapse the redirect chains.
Coding agents aren't slow because they're dumb. They're slow because every edit waits on the model.
We gave OpenCode a real feature request on TodoMVC. DeepSeek V4.1 Flash on Wally shipped drag-to-reorder in about a minute, first try.
Waiting is optional now.
DeepSeek V4.1 Flash vs real CSSBattle targets.
It looks at the picture, writes the HTML and CSS, and the first two matched in under two seconds each.
Frontend devs, it's been an honor.