LongCat-2.0 is an MIT-licensed 1.6T-parameter MoE model with ~48B active parameters, 1M context, LongCat Sparse Attention, and post-training for coding and agentic workflows. It was trained on AI ASIC superpods and integrates with Claude Code, OpenClaw, and Hermes.
LongCat-2.0 is a 1.6T-parameter MoE model with about 48B active parameters per token, 1M context, and open weights under MIT.
But it was not trained in the usual Nvidia-heavy way. The full training run was built on AI ASIC superpods, over more than 35T tokens, with no rollback or irrecoverable loss spike.
Training a trillion-scale model is already hard. Getting that run stable on alternative hardware is probably the more interesting story here 🤔
About LongCat-2.0 on Product Hunt
“1.6T MoE trained entirely on AI ASICs”
LongCat-2.0 launched on Product Hunt on July 7th, 2026 and earned 130 upvotes and 27 comments, placing #10 on the daily leaderboard. LongCat-2.0 is an MIT-licensed 1.6T-parameter MoE model with ~48B active parameters, 1M context, LongCat Sparse Attention, and post-training for coding and agentic workflows. It was trained on AI ASIC superpods and integrates with Claude Code, OpenClaw, and Hermes.
On the analytics side, LongCat-2.0 competes within Open Source and Artificial Intelligence — topics that collectively have 541.5k followers on Product Hunt. The dashboard above tracks how LongCat-2.0 performed against the three products that launched closest to it on the same day.
Who hunted LongCat-2.0?
LongCat-2.0 was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of LongCat-2.0 including community comment highlights and product details, visit the product overview.
Zac Zuo
Hi everyone!
LongCat-2.0 is a 1.6T-parameter MoE model with about 48B active parameters per token, 1M context, and open weights under MIT.
But it was not trained in the usual Nvidia-heavy way. The full training run was built on AI ASIC superpods, over more than 35T tokens, with no rollback or irrecoverable loss spike.
Training a trillion-scale model is already hard. Getting that run stable on alternative hardware is probably the more interesting story here 🤔