Gradient Network

Gradient Network builds infrastructure to run AI at scale. They've got two core things going: Echo-2, a distributed reinforcement learning framework that decouples learning from rollouts using async RL, letting you orchestrate GPUs across unreliable machines without the coordination headache. Then there's Parallax, which turns a messy pool of GPUs and Apple Silicons into a unified LLM serving platform that handles both inference and training efficiently.

On the practical side, they offer a sovereign AI OS—host and run open source models locally with full control and privacy, completely free. Teams that want managed access can run an LLM gateway with API compatibility across models such as Kimi K2, GLM-4, MiniMax M2, and DeepSeek V3.2, all at aggressive pricing ($0.28-$0.48 per input token, depending on the model).

This matters for AI researchers and data teams because distributed RL at scale is genuinely hard—coordinating rollouts across unreliable hardware without staleness killing your training is where most people fail. Gradient's approach lets you actually use the hardware you have. For teams in Malaysia and Singapore building AI products, having both the open-source self-hosted option and the managed API gives you the flexibility to start local and scale up without vendor lock-in.

The framework is built for researchers who need reproducibility and control, but the infrastructure is practical enough for production workloads. They're solving real infrastructure gaps in the distributed ML space.