NVIDIA open-sources full Run:ai platform under Apache 2.0

🚀 RELEASES & LAUNCHES

• NVIDIA open-sources full Run:ai platform under Apache 2.0 as part of DSX AI Factory
  NVIDIA released the complete Run:ai AI workload and GPU orchestration platform as open source under Apache 2.0.

  • Aimed at AI cloud providers, platform teams, and enterprises running training and inference workloads on Kubernetes.
  • The release includes the full platform plus four modular components: KAI Scheduler, GPU Fractioning Operator, KAI Resource Management, and Workload Map, adoptable independently.
  • Source code is on GitHub (dsx-ai-factory/run-ai) under Apache 2.0, with a Helm-chart Community Product on NVIDIA NGC requiring no subscription or support entitlement.
  • KAI Scheduler was also contributed to CNCF as a Sandbox project.
  • Eight DSX ecosystem partners — Crusoe, Mirantis, Nebius, OpenNebula, Rafay, Saturn Cloud, Spectro Cloud, and vCluster — are integrating the platform into their offerings.
  • Open sourcing gives builders production-grade GPU orchestration to adopt, extend, or embed without a paid Run:ai subscription; NVIDIA-supported commercial offerings remain available.
  Sources 📄 Source: github.com · 📎 Coverage: developer.nvidia.com · 👁 via r/LocalLLaMA

• Xyntetik Runner trains LoRA adapters directly on quantized GGUF weights, byte-reproducibly
  A developer released Runner, a C runtime that trains LoRA adapters directly through the quantized GGUF weights it serves, with byte-identical reproducible adapters.

  • For local-LLM users and hobbyists: no PyTorch, no FP16 parent copy, no separate training runtime — just the GGUF and your data.
  • Runner (single-binary C runtime, CPU/CUDA/Metal, OpenAI-compatible) trains a tool-use LoRA on a frozen Qwen3-4B Q4_K_M GGUF; exact tool-call accuracy rose from 0.69 to 1.00 on a 29-prompt held-out eval.
  • Two independent 4B-scale runs produced byte-identical adapters (same SHA-256); an external check reproduced the same one-step loss (0.676138) and adapter SHA on CPU and a Tesla T4.
  • BF16- and Q8_0-trained adapters were near-identical (cosine 0.9998), while the Q4_K_M adapter followed a measurably different path (cosine 0.9926) yet matched 1.00 on the task.
  • Author notes limits: narrow synthetic memorization-scale task, so it demonstrates the mechanism and reproducibility, not general capability gains.
  Sources 📄 Source: github.com · 📎 Coverage: discuss.huggingface.co · 👁 via r/LocalLLaMA