NVIDIA

NVIDIA said GeForce RTX and related devices can run LTX-2 and updated ComfyUI for local AI video generation up to 3x faster with up to 60% lower VRAM use. The post attributes this to PyTorch-CUDA optimizations, native NVFP4/FP8 support in ComfyUI, and an RTX Video 4K upscaling node due next month; LTX-2 open weights are available now and the workflow ships next month. The real signal for AI builders is that local 4K video is shifting from VRAM-bound demos to usable RTX workflows.

At CES 2026, NVIDIA said its six-chip Rubin AI platform is now in full production and cuts token generation cost to about one-tenth of the prior platform. The post cites 50 petaflops NVFP4 inference for Rubin GPUs, 5x gains from its KV-cache storage tier, and the new open autonomous-driving model family Alpamayo; the key signal is production status and cost curve, not the “AI everywhere” framing.

NVIDIA introduced Rubin-based DGX SuperPOD systems, with DGX Vera Rubin NVL72 and DGX Rubin NVL8 slated for the second half of this year. One DGX SuperPOD can combine eight NVL72 systems for 576 Rubin GPUs, 28.8 exaflops FP4, and 600TB memory; NVIDIA says inference token cost drops by up to 10x versus the prior generation. The key detail is rack-scale design: 260TB/s NVLink per rack, which the post says removes model partitioning.

NVIDIA showed at CES that DGX Spark and DGX Station can run 100B to 1T-parameter models locally on deskside systems. The post cites a 35% average llama.cpp speedup, up to 70% NVFP4 compression, 775GB coherent memory on DGX Station, and a 250,000 token/sec pretraining demo. The real signal is the local dev loop: fine-tuning, inference, RAG, coding assistants, and robotics demos all target replacing some cloud iteration with deskside compute.

NVIDIA said the new Mercedes-Benz CLA will be the first U.S. vehicle to ship DRIVE AV with enhanced Level 2 point-to-point driver assistance by the end of this year. The post describes a dual-stack design: end-to-end AI for core driving plus a classical safety stack built on Halos, with OTA upgrades, urban navigation, active collision avoidance, and automated parking. The launch timing is specific, but the post does not disclose pricing, sensor configuration, or the exact ODD.

NVIDIA released open models, datasets and training tools spanning Nemotron, Cosmos, Alpamayo, Isaac GR00T and Clara, plus 10T language tokens, 500K robotics trajectories, 455K protein structures and 100TB of vehicle sensor data. Newly disclosed items include Nemotron Speech/RAG/Safety, Cosmos Reason 2, Transfer 2.5, Predict 2.5, GR00T N1.6 and Alpamayo 1; the key signal is that NVIDIA is opening the data stack across agents, physical AI, AVs and biomedicine.

Mistral AI released the Mistral 3 family: three dense models at 14B, 8B and 3B, plus Mistral Large 3, which uses a sparse MoE architecture with 41B active and 675B total parameters. All are open-sourced under Apache 2.0.

OpenAI and NVIDIA signed a letter of intent to deploy at least 10 gigawatts of NVIDIA systems for OpenAI’s next-generation AI infrastructure. NVIDIA plans to invest up to $100 billion into OpenAI as each gigawatt is deployed, and the first 1 GW phase is targeted for H2 2026 on the Vera Rubin platform. The key detail is execution: this is still an LOI, and final terms are not yet closed.

OpenAI, NVIDIA, and Nscale launched Stargate UK, with OpenAI exploring offtake of up to 8,000 GPUs in Q1 2026 for UK sovereign compute. The project may scale to 31,000 GPUs over time for public services, finance, research, and national security use cases that require local jurisdiction. The key detail is local compute for regulated workloads; pricing, full site capacity, and launch timing are not disclosed.

OpenAI and Oracle agreed to add 4.5 GW of Stargate data center capacity in the U.S., bringing capacity under development to over 5 GW and more than 2 million chips. OpenAI says this advances its January pledge to build 10 GW of U.S. AI infrastructure with $500 billion over four years, and it now expects to exceed that target. The concrete signal is deployment: Stargate I in Abilene has started receiving Nvidia GB200 racks and is already running early training and inference workloads.