Would love to here which tasks would you send to which of them...
Of course you would say the more complex to Fable, but most of the hard real world tasks I I gave to Kimi were successful.
I am also missing the /loop in Devin, but this may be worth another post (?)
Is chaining multiple GPUs efficient these days?
Are there good tools for virtualization for that?
Is it cheaper than buying a monster?
I’m a SaaS/AI founder and I’m trying to understand the real requirements to host the larger open‑source models (DeepSeek, Qwen, Kimi‑style models) on my own infra instead of using hosted APIs.
+4
If you’ve done this in production or a serious homelab:
– What VRAM / GPU setup are you using, and what did it cost?
– Did you go on‑prem or rent GPUs (RunPod, Lambda, etc.)?
– What ended up being the real bottleneck: cost, ops complexity, or model performance?
Any “if I were starting today, I’d do X instead of Y” stories would be super helpful.
I’m a SaaS/AI founder and I’m trying to understand the real requirements to host the larger open‑source models (DeepSeek, Qwen, Kimi‑style models) on my own infra instead of using hosted APIs.
+4
If you’ve done this in production or a serious homelab:
– What VRAM / GPU setup are you using, and what did it cost?
– Did you go on‑prem or rent GPUs (RunPod, Lambda, etc.)?
– What ended up being the real bottleneck: cost, ops complexity, or model performance?
Any “if I were starting today, I’d do X instead of Y” stories would be super helpful.
After a few days using Kimi K2.6 in Windsurf on a real codebase, it crossed the line from “cool demo” to “this actually saves time”: long‑context, solid multi‑file edits, and agentic flows that usually finish the job.
Anyone else pushing it on bigger projects – where does it start to crack for you?
The frontier labs (OpenAI, Anthropic, Google) spent years competing mainly with each other on benchmarks.
That's changing. Chinese open-weight models — DeepSeek, Qwen, Kimi — are now good enough to be embedded directly into Western developer products. Cursor and Windsurf already offer them as options alongside GPT-4o and Claude.
The competitive threat isn't "China beats GPT-5." It's subtler: product companies can now build on Chinese open-weight models instead of paying OpenAI/Anthropic API rates. That weakens model-provider lock-in and turns raw capability into a more commoditized layer.
The strategic fight is no longer only about benchmark leadership — it's about who controls distribution, cost, and developer adoption.
Curious what this community thinks: is model commoditization already happening in production, or are frontier labs still irreplaceable for serious use cases?