Nvidia RTX 5090

Headlines · 6

1. 24h
   VAST open-sources EdenLive, a real-time world model renderer

   VAST released and open-sourced EdenLive, a real-time generative renderer that is a stage result of its Project Eden world-model research. According to VAST, EdenLive takes a simplified proxy scene or reference video, a target first frame and a text prompt, and continuously renders photorealistic or stylized frames along the reference's layout and motion, while an upstream interaction system maintains game rules and world state. VAST says the 5B model exceeds 24 FPS at 720P on a single Nvidia RTX 5090, can generate continuously for minutes and has end-to-end latency in the hundreds of milliseconds. The release includes model weights at multiple sizes, inference code, a technical report and Game Prototype Turbo, a prompt-to-proxy-game skill; the team also acknowledged remaining limits in multi-character identity consistency, small-object control and long-run visual consistency.

   量子位（原生 RSS） · 🔥 10
2. 3d
   ORL open-sourced: diffusion LLM hits 2,788 tokens/s on a single RTX 5090

   A research team posted ORL (On-Policy Residual Learning), a method for making diffusion language models converge in fewer forward passes so they can finalize more tokens per step, and open-sourced the models, training code, evaluation pipeline and a custom inference engine called dlmrt. The authors self-report 2,788 tokens/s for LLaDA-2.1-mini plus ORL in NVFP4 on a single RTX 5090, 7,708 tokens/s for DiffusionGemma 26B MoE on 8× B200, up to 6.9× higher end-to-end throughput on QwenDLM-4B, and a HumanEval pass@1 rise from 82.9% to 89.6%. They caution that all figures are batch-size-1 single-request peaks rather than averages over arbitrary prompts or aggregate throughput, and that the DFlash and EAGLE3 comparisons are end-to-end system tests in which ORL runs on dlmrt while the speculative decoders run on vLLM.

   r/MachineLearning · 🔥 0
3. 3d
   Tsinghua open-sources Puro-2B, a low-cost pretraining recipe trained on RTX 5090s

   Tsinghua University researchers led by Chen Wenguang and Lü Kaifeng have released Puro-2B, an end-to-end open pretraining package that includes training data, framework, configuration and model weights. The team trained a roughly 2-billion-parameter model from scratch on a cluster of consumer RTX 5090 GPUs, using up to 1.4 trillion tokens. It reports that a version with about $4,400 in pretraining compute cost beat Qwen2-1.5B on the average of 15 tasks, while a $6,900 best version came close to Qwen2.5-1.5B; these are team-reported results without independent replication.

   36氪 人工智能 · 🔥 0
4. 4d
   Kunlun open-sources Matrix-Game 4.0, real-time game world model on one RTX 5090

   Kunlun Tech's Skywork AI released and open-sourced Matrix-Game 4.0 on October 15, describing it as the world's first game world model that runs in real time on a single consumer GPU. The system splits the work: a coding agent generates the game code, the game engine maintains world state and interaction rules and emits a structural frame as a constraint, and a video model renders the final image in real time — a loop the company says runs from "code-generated game logic to structural constraints to live video rendering." Kunlun says it delivers 480p at 60 FPS on one NVIDIA RTX 5090 with under 150 ms input-to-frame latency and about 18.5GiB peak VRAM; planned work includes a refiner for higher resolution, consistent multi-player viewpoints and more game genres. Code and models are published on GitHub, Hugging Face and a project site.

   IT之家 AI · 🔥 0
5. Oct 8
   MiniMax H3 live video generation on a single RTX 5090

   A Comfy blog post presents ComfyStreamerH3, an open-source custom ComfyUI node that runs MiniMax H3 on one 32GB RTX 5090, producing 15 seconds of 448×256 video in 15 seconds or less with a four-step setup. The listed optimizations include sparse attention that applies full attention to only about 20% of video blocks, NicoLab28's ClipProj adapting Qwen3-VL-4B as a stand-in for H3's 32B text encoder (about 4.5GB vs 15.7GB), FastVideo's pruned INT8 checkpoint, a fused FP4 MLP, and Kijai's INT8 tiled decoder overlapped with NVENC encoding, cutting VRAM needs from 80GB to under 30GB. The author says running such models continuously can cost upwards of $6,000 per day while RTX 5090 rentals start near 70 cents an hour, and notes FastVideo's Preview V1 took 47.2 seconds for a 15-second 1344×768 clip on one B200. The clips still show visible artifacts, and the author suggests passing them through a cheap upscaler such as SolRefinery.

   Comfy Blog · 🔥 0
6. Oct 6
   Long-WAM scales world-action model context for real-time robot control

   Long-WAM is a model-system framework for scaling the context of causal world-action models under real-time robot-control constraints; its authors report that longer histories only pay off when the video foundation is pretrained autoregressively. On RoboCasa GR-1, raising context from 0.0 to 19.2 seconds lifted success from 63.3% to 78.7%, while a bidirectionally pretrained initialization showed no net gain, and robot-domain autoregressive pretraining further raised peak success on GR-1 and LIBERO-Long. The paper also reports deployment on RTX 5090, DGX Spark and Jetson AGX Thor, with each action chunk including future-video latent prediction taking 107.4 ms on RTX 5090, plus real-time long-horizon manipulation on Unitree G1 and YAM reaching 95% success on dynamic cup stacking, where it says Pi0.5 and Fast-WAM succeeded in none of 20 trials.

   Hugging Face · Papers · 🔥 0

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