The only difference between using a slower chip such as H100 (or Huawei's Ascend 750) vs NVIDIA's newer Blackwell chips (B200 etc) is that you need more of the slower chips to achieve the same total FLOPs in your cluster. It has zero effect on what models you can run on it.
To quote Pat Toulme:
There’s a big misconception about how GLM 5.2 was trained. Yes, they distilled Claude and GPT 5.5 — but distillation is not how they matched Opus quality. Distillation only fixed the cold start problem in RL.
RLing an agentic coding model isn’t rocket science. In simplified terms:
1. RL needs trajectories — rollouts where the model actually completed a task in some env
2. No successful trajectory on a task = zero gradient = you can’t RL it. This is the cold start problem
3. Distillation solves it. You seed your model with knowledge from a smarter one (Claude, GPT) on tasks it can’t do yet
4. Now it produces positive trajectories on those tasks
5. RL on those trajectories and hill climb agentic coding
6. At that point you no longer need to distill and can solely hill climb RL to better models
This is an interesting curve. I’d argue it’s harder to get to Opus 4.8 from scratch than to go from Opus 4.8 → Fable/Mythos tier.
GLM 5.2 is already producing positive trajectories, so they have plenty to RL on — they’ll keep climbing to Mythos quality without distilling any further. They no longer need American models.
https://x.com/PatrickToulme/status/2069211575437627743
Not exactly sure what the finish line in "the race to superintelligence" looks like and even moreso it's unclear why you think being there first is a critical benefit.