NVIDIA invests in chip-portability startup Decart at ~$4B valuation

Nvidia investing in a chip-portability layer is the surprise of the week. The conventional read: Nvidia is hedging against a future where buyers demand multi-vendor inference (TPUs, Trainium, MI series) and would rather own a piece of the abstraction than be commoditized by it. The contrarian read: Decart's portability play is more meaningful for training optimization than chip-swapping, and Nvidia is buying telemetry on what workloads do flee.

Karpathy's participation gives the deal credibility with the research-engineer audience — he rarely angel-invests outside research-grade infra. Adobe and Toyota signal media and automotive end-customer intent.

Nvidia separately deepened its Fanuc partnership using Isaac Lab and Omniverse for robot simulation, and Arrive AI is using Isaac Sim + Blackwell GPUs for autonomous drone delivery. PyTorch 2.11 also shipped CUDA-enabled aarch64 Linux wheels directly from PyPI in collaboration with vLLM — removing a long-standing friction for ARM-based GPU servers. The flagship PRO GPU is quietly crossing the $10,000 price barrier amid sustained AI demand.

Skeptical take (r/MachineLearning, 526 upvotes on a separate thread): independent MLPerf numbers on Blackwell Ultra's claimed 2.3x throughput over Hopper are still missing. Until those land, the benchmark narrative remains vendor-supplied. Watch next: whether Decart's customer list grows beyond pilots, and whether Nvidia's investment is followed by an official Hopper-Blackwell migration tooling release that overlaps Decart's space — which would clarify intent.