4 stories
NVIDIA NeMo RL applies FP8 precision across both generation and training phases of reinforcement learning, closing accuracy gaps via importance sampling and adding 48% total speedup when KV cache and attention are also quantized.
NVIDIA has integrated the Muon higher-order optimizer into Megatron Core and NeMo, showing minimal throughput loss versus AdamW on GB300 hardware while enabling training at thousands of GPUs.
The latest NVIDIA FLARE API splits federated learning adoption into two steps: a minimal client API that adds federation to existing training scripts without restructuring them, and portable job recipes that run unchanged from simulation to production.
NVIDIA outlines how its Blackwell platform and NIM microservices support DeepSeek-V4's million-token context requirements, with initial GB200 NVL72 benchmarks and deployment paths via SGLang, vLLM, and hosted endpoints at build.nvidia.com.