Image created with gemini-2.5-flash-image with claude-sonnet-4-5-20250929. Image prompt: A polished silicon wafer on a sleek slate-grey pedestal, styled as a minimalist birthday cake with two lit candles casting colorful reflections across its mirror-like surface and etched circuit patterns. Cinematic studio photography with high-contrast lighting, deep blues and rich reds accent the celebratory scene, crisp white highlights, modern and elegant composition.
Nvidia just released Lyra on Hugging Face Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation TL;DR: Feed-forward 3D and 4D scene generation from a single image/video trained with synthetic data generated by a camera-controlled video diffusion model https://x.com/_akhaliq/status/1970949464606245139
Oracle, Nvidia, Microsoft, Coreweave and Broadcom are close to half a trillion dollars in infrastructure investments for OpenAI one more OOM isn’t far off give it a few years and we will hit 7 trillion”” / X https://x.com/scaling01/status/1970543749727166600
How does a (then) 2-year-old startup with NO commercial product raise $675M from Jeff Bezos, OpenAI & NVIDIA? 🤯 This is the untold story of @Figure_robot. And @adcock_brett. It’s a masterclass in building a reality-distortion field that convinced the world’s smartest https://x.com/IlirAliu_/status/1969748812437549265
We’re excited to share that NVIDIA is investing in ElevenLabs, with support from Jensen Huang. Last week’s U.S. state visit to the UK strengthened AI ties. With our roots growing deeper in both places, this partnership and conversation were the perfect way to cap it off. https://x.com/matistanis/status/1970185470182047788
Abundant Intelligence – Sam Altman https://blog.samaltman.com/abundant-intelligence
Grateful to Jensen for the almost-decade of partnership!”” / X https://x.com/sama/status/1970483993486217258
In case anyone was wondering, 10GW is about 6% of the energy that all humans in the world spend thinking.”” / X https://x.com/gneubig/status/1970449455846768701
OpenAI Shows Us The Money – by Zvi Mowshowitz https://thezvi.substack.com/p/openai-shows-us-the-money
Our vision is simple: we want to create a factory that can produce a gigawatt of new AI infrastructure every week.”” — @sama, in reference to OpenAI”” / X https://x.com/kevinweil/status/1970519868324860145
10GW is about $340B of nvidia h100 at $30k/gpu (assuming 20% of power for non-gpus). if openai got a 30% volume discount, they’d pay nvidia $230b probably. so instead, maybe openai pays nvidia full price and nvidia invests the excess $100B into openai stock 😬 (just throwing”” / X https://x.com/soumithchintala/status/1970464906072801589
looking forward to what we’ll build together with NVIDIA!”” / X https://x.com/gdb/status/1970299081999426016
More compute in the making. Announcing 5 new Stargate sites with Oracle and SoftBank, putting us ahead of schedule on the 10-gigawatt commitment we announced in January. https://x.com/OpenAI/status/1970601342680084483
OpenAI & NVIDIA Announce Strategic Partnership to Deploy 10GW of NVIDIA Systems This enables OpenAI to build & deploy at least 10 gigawatts of AI datacenters with NVIDIA systems representing millions of GPUs for OpenAI’s next-gen AI infrastructure. https://x.com/OpenAINewsroom/status/1970157101633990895
OpenAI and NVIDIA Announce Strategic Partnership to Deploy 10 Gigawatts of NVIDIA Systems | NVIDIA Newsroom https://nvidianews.nvidia.com/news/openai-and-nvidia-announce-strategic-partnership-to-deploy-10gw-of-nvidia-systems
OpenAI and NVIDIA announce strategic partnership to deploy 10 gigawatts of NVIDIA systems | OpenAI https://openai.com/index/openai-nvidia-systems-partnership/
Together, NVIDIA and OpenAI are expanding the frontier of AI — transforming nearly every industry and unlocking use cases once unimaginable. “There’s no partner but NVIDIA that can do this at this kind of scale, at this kind of speed,” said @OpenAI CEO Sam Altman. https://x.com/nvidianewsroom/status/1970223778937586043
Announcing strategic partnership with @nvidia for millions of GPUs — about as much compute as they’ve shipped in 2025 in total — and an investment up to $100B as these GPUs are deployed: https://x.com/gdb/status/1970173243350008201
For both $NVDA and OpenAI, the $100B $NVDA investment is perfect: 1. For OAI, the biggest question was how they were going to raise the future +$300B as the valuation is already very high, and the cash burn for the next few years is projected to be crazy. On top of it, there is”” / X https://x.com/rihardjarc/status/1970170005858726278
so let me get this right: Oracle says Openai committed $300B for cloud compute → oracle stock jumps 36% (best day since 1992) Oracle runs on Nvidia GPUs → has to buy billions in chips from Nvidia Nvidia just announced they’re investing $100B into openai Openai uses that”” / X https://x.com/SullyOmarr/status/1970176527137718654
@techdevnotes Just as we will be the first to bring a Gigawatt of coherent training compute online, we will also be the first to 10GW, 100GW, 1TW, …”” / X https://x.com/elonmusk/status/1970358667422646709
Cohere hits $7B valuation a month after its last raise, partners with AMD | TechCrunch https://techcrunch.com/2025/09/24/cohere-hits-7b-valuation-a-month-after-its-last-raise-partners-with-amd/
Many people think LLMs are non-deterministic. This is often not true! You just need 3 lines of code to make your LLM deterministic LLMs (as any PyTorch model) are non-deterministic only when they include certain operations or when using multiple GPUs Try the code yourself https://x.com/gabriberton/status/1968559505966350705
Really cool to see that Anthropic also uses JAX for inference on Google TPU. I’m curious whether they also use JAX for inference on GPU’s (Azure/AWS) or if they developed a separate codebase for it.”” / X https://x.com/borisdayma/status/1968697704361468354
At AMD, we believe in Cohere not just as a partner, but also as a customer and investor. Now we’re expanding our collaboration: ✔ Cohere models on AMD Instinct GPUs ✔ AMD adopts North internally ✔ Together delivering trusted, sovereign-ready AI The future of enterprise AI is https://x.com/AMD/status/1970824479279317446
Today, we’re shipping native support for context-parallelism to help make diffusion inference go brrr on multiple GPUs 🚀 Our CP API is made to work with two flavors of distributed attention: Ring & Ulysses. Huge thanks to @aryanvs_ for shipping this! Deets ⬇️ https://x.com/RisingSayak/status/1971154049698509190
We beat Nvidia’s cuBLAS kernels on B200s in 170 LOC. Using zero CUDA. Just pure Mojo. Here’s exactly how we went from 1% to 106% of Nvidia benchmark perf from scratch (with code) 👇🧵 https://x.com/AliesTaha/status/1970510268745896036
Congrats to @useblacksmith on their $10M Series A! More than 12,000 devs across 800+ companies rely on Blacksmith for their CI. Customers have run over 1 billion vCPU-minutes on their infra, the equivalent of 2000 years of nonstop compute time. https://x.com/ycombinator/status/1968701548135071772
Oracle Corporation Announces Promotion of Clay Magouyrk and Mike Sicilia to CEOs https://www.oracle.com/news/announcement/oracle-corporation-announces-promotion-of-clay-magouyrk-and-mike-scilia-2025-09-22/
We raised $250M to accelerate building AI’s unified compute layer! 🔥 We’re now powering trillions of tokens, making AI workloads 4x faster 🚀 and 2.5x cheaper ⬇️ for our customers, and welcomed 10K’s of new developers 👩🏼💻. We’re excited for the future! https://x.com/Modular/status/1970881293933273524
Oracle eyes $20 billion AI cloud computing deal with Meta, source says | Reuters https://www.reuters.com/business/oracle-talks-with-meta-20-billion-ai-cloud-computing-deal-bloomberg-reports-2025-09-19/
Progress at our datacenter in Abilene. Fun to visit yesterday! https://x.com/sama/status/1970812956733739422
A much improved model scheduling system is now on Ollama! – 🫶 Significantly reduced crashes due to out of memory issues – 📍 Maximizing GPU utilization – 🚵 Multi-GPU performance – 🌎 Accurate reporting of memory usage Learn more & try the latest Ollama! 👇👇👇 https://x.com/ollama/status/1970591425566806231
Paving the way for unlimited context windows. Introducing @zml_ai /attnd: sparse logarithmic attention, on CPU, over UDP, faster than GPU. https://x.com/steeve/status/1971126773204279495
Congrats to @deepseek_ai ! DeepSeek-R1 was published in Nature yesterday as the cover article, and vLLM is proud to have supported its RL training and inference🥰 https://x.com/vllm_project/status/1968506474709270844
Finetune DeepSeek 🐳 with two Mac Studios + MLX 🚀 We use pipeline parallelism to split the full 671GB model across two devices connected by a single TB5 cable. LoRA reduces the number of parameters to train from 671 billion down to 37 million, reducing the memory overhead from https://x.com/MattBeton/status/1968739407260742069
Nature Portfolio also addressed this on Zhihu: Publishing this paper is itself a significant milestone👏 🤔 DeepSeek-R1 learns step-by-step reasoning with minimal human help: • Reinforcement learning: correct answers get rewards, mistakes penalized • Learns to self-verify & https://x.com/ZhihuFrontier/status/1968603082167828494
Pro Tip💡Fast and simple way to deploy DeepSeek-V3.1-Terminus with vLLM ⚡️ Run it with: vllm serve deepseek-ai/DeepSeek-V3.1-Terminus -tp 8 -dcp 8 (as simple as appending -dcp 8 after -tp 8) Thanks to the @Kimi_Moonshot team, vLLM 0.10.2 adds Decode Context Parallel (DCP) https://x.com/vllm_project/status/1970814441718755685
PSA: you can run the new DeepSeek v3.1 Terminus on a single M3 Ultra with mlx-lm at very usable speed. 4-bit quant one-shotted space-invaders in HTML/CSS: https://x.com/awnihannun/status/1970151204102750573
FP8 at home using 6000 Blackwell on PCIe4 (bc money went to the GPUs): Tangible benefits exist at PP=2/DP=2 Llama 8B, seq_len 4096 Single GPU: BF16 == 100 TFLOPs FP8 == 77 TFLOPs 2x GPU (Z2) BF16 == 78 TFLOPs FP8 == 85 TFLOPs 🔥”” / X https://x.com/TheZachMueller/status/1970262732319412599
Nvidia presents ReaSyn: Rethinking molecule synthesizability • Treats synthesis like CoT with Chain-of-Reaction (CoR) steps • Each reaction = reasoning step → richer supervision & step-by-step learning • Adds RL finetuning + test-time scaling for better optimization • SOTA https://x.com/arankomatsuzaki/status/1969976462091645144
there’s also an argument to be made that these are good deals. what would nvidia do with all that free paper money (which would’ve been a discount), might as well deploy it into the most successful AI company in the market if the thesis is there’s still massive upside, while”” / X https://x.com/soumithchintala/status/1970466276687380922
Modular’s GenAI stack now provides the world’s best Blackwell and MI355X performance, as well as developer support for Apple/NV/AMD consumer GPUs. Never before has it been this easy to get going, and unified pip package gives you choice of how to deploy! Check it out👇🚀”” / X https://x.com/clattner_llvm/status/1970203319097495891
NEWS: Nvidia is considering a $500 million investment in UK self-driving startup Wayne. Here is a video of a Wayve vehicle that uses 7 cameras, no radar, no LIDAR, no HD maps and no labeling. All an end-to-end AI-enabled foundation model. Wayve currently operates in Britain and https://x.com/sawyermerritt/status/1969049038302409016
Nvidia spent over $900 million on Enfabrica CEO, AI startup technology https://www.cnbc.com/2025/09/18/nvidia-spent-over-900-million-on-enfabrica-ceo-ai-startup-technology.html
@NVIDIA contributes extensively to open-source models on Hugging Face – with over 57 collections published – most within the past year: https://x.com/PavloMolchanov/status/1970553850173255895
An open-source extension for LLM serving engines – LMCache It’s like a caching layer for large-scale, production LLM inference. LMCache implements smart KV cache management, reusing key–value states of previously seen text across GPU, CPU and local disk. It can reuse any https://x.com/TheTuringPost/status/1971318599253098559
Inference Providers @huggingface powered by @novita_labs supports Qwen3-VL, the bleeding-edge vision LM 🔥 the model is quite large (22B active 235B total params) so this makes it super easy to try 💚 https://x.com/mervenoyann/status/1971168938848551021
Serving a model at scale is hard. Serving it across three hardware platforms (AWS Trainium, NVIDIA GPUs, Google TPUs) while maintaining strict equivalence is a whole other level. Makes you wonder if the hardware flexibility is truly worth the hit to development speed and https://x.com/_philschmid/status/1968586407548518565
