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Topic: "deterministic-inference"
NVIDIA to invest $100B in OpenAI for 10GW of Vera Rubin rollout
qwen3-omni deepseek-v3.1 nvidia openai oracle intel enfabrica wayne gpu-infrastructure deterministic-inference reinforcement-learning fp8-precision gpu-performance ai-infrastructure strategic-partnerships investment datacenters cuda-graphs pipeline-parallelism data-parallelism artificialanlys gdb
NVIDIA and OpenAI announced a landmark strategic partnership to deploy at least 10 gigawatts of AI datacenters using NVIDIA's systems, with NVIDIA investing up to $100 billion progressively as each gigawatt is deployed, starting in the second half of 2026 on the Vera Rubin platform. This deal significantly impacts the AI infrastructure funding landscape, potentially supporting OpenAI's $300 billion commitment to Oracle. The announcement caused major stock market reactions, with NVIDIA's market cap surging by $170 billion. Additionally, advancements in deterministic inference for reinforcement learning and FP8 precision gains in GPU performance were highlighted by AI practitioners.
Oracle jumps +36% in a day after winning $300B OpenAI contract
qwen3-235b qwen3-4b qwen2.5-7b vllm oracle openai microsoft moonshot-ai vllm-project thinking-machines-lab meta reinforcement-learning model-weight-updates deterministic-inference benchmarking long-context model-optimization cuda distributed-training kimi_moonshot arankomatsuzaki qgallouedec cHHillee woosuk_k stasbekman
Oracle's OCI division reported a stunning +359% revenue bookings growth to $455B with cloud revenue guidance of $144B by 2030, driven significantly by a large deal with OpenAI amid tensions with Microsoft. On AI infrastructure, Moonshot AI released Kimi’s checkpoint-engine, enabling rapid weight updates on 1T-parameter models across thousands of GPUs, integrating with vLLM. RLFactory introduced a plug-and-play reinforcement learning framework for tool-using agents, showing smaller models outperforming larger ones. TRL v0.23 added context parallelism for long-context training. Thinking Machines Lab published research on deterministic inference pipelines, making vLLM deterministic for Qwen models. Meta launched BackendBench, a PyTorch benchmarking tool.