Kimi V4 is headed for native multimodality, but Moonshot says compute is the limit
teortaxesTex · x · 2026-07-23
Moonshot’s Kimi V4 is said to be heading toward native multimodality, and the image-backed quote says V4 is already as large as the team can train right now.
Key points from the post and image:
- Kimi V4 and its follow-up versions are expected to support native multimodality.
- The model is described as being trained at the largest size the team can currently afford.
- The implied bottleneck is compute, not lack of willingness to scale.
- The post also suggests that a successful funding round could let Moonshot train larger models and push further on scaling.
The broader message: Kimi is signaling both a multimodal roadmap and a very explicit compute constraint.
More from Infra
- Google’s compute fight pits Cloud against Gemini, Search and YouTube — sudoraohacker · 2026-07-23
- MiniMax says MI355X is nearing B200 for serving its 428B multimodal model — salykova_ · 2026-07-23
- Aurora launches an open-source Go gateway for routing and securing LLM traffic — Select-Medicine-9310 · 2026-07-23
- Investors betting against infrastructure spending are missing the current AI cycle — cgarciae88 · 2026-07-23
- TileLang debate says leaving CUDA could cut inference costs with only 1–2% loss — teortaxesTex · 2026-07-23
- Why inference is becoming the bigger AI hardware story than training — Genzinvestor16180339 · 2026-07-23