NVIDIA AI(@NVIDIAAI)

From autonomous vehicles to smarter factories, companies are putting physical AI to work. We’ve add...

NVIDIA AI (@NVIDIAAI) — Fri, 23 Oct 2026 18:21:15 GMT

From autonomous vehicles to smarter factories, companies are putting physical AI to work.

We’ve added new NVIDIA Cosmos tools to help developers generate training data, train world models and test them before deployment.

See how @Porsche, @MercedesBenz, @encord_team, @Siemens, @fogsphere, and @ltx_io are using Cosmos across robotics, autonomous driving and factory operations 👇

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NVIDIA AI (@NVIDIAAI) — Mon, 19 Oct 2026 17:08:11 GMT

x.com/jackson_stokes…

Jackson Stokes@jackson_stokes

We partnered with @nvidia, @nollahealth, and @nebiusai to post train Nemotron 3.5 VL Super far past the frontier on dermatology diagnosis. 

The model set new standards in our internal measures of trainability, and with Nolla it's powering the future of AI in medical care.

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Road to GTC Berlin: Europe’s AI Builders in the Spotlight https://t.co/2mcl76sSUs

NVIDIA AI (@NVIDIAAI) — Mon, 19 Oct 2026 12:30:09 GMT

Road to GTC Berlin: Europe’s AI Builders in the Spotlight x.com/i/broadcasts/1…

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Ron is back!

NVIDIA AI (@NVIDIAAI) — Sun, 18 Oct 2026 16:59:42 GMT

Ron is back!

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Already running a model endpoint? What does it take to scale it on Kubernetes? In five minutes, we ...

NVIDIA AI (@NVIDIAAI) — Sun, 18 Oct 2026 16:57:51 GMT

Already running a model endpoint? What does it take to scale it on Kubernetes?

In five minutes, we walk through how NVIDIA Dynamo handles deployment with scalable workers and an OpenAI-compatible endpoint.

Watch below:

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That's a wrap on Build-A-Claw Berlin! And we of course served @lobsterlager. A father/son duo used...

NVIDIA AI (@NVIDIAAI) — Sun, 18 Oct 2026 08:01:48 GMT

That's a wrap on Build-A-Claw Berlin! 

And we of course served @lobsterlager. A father/son duo used @openclaw to make a lager.

Thank you to everyone who stopped by and we're looking forward to an amazing week at @NVIDIAGTC Berlin.

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@AlicanKiraz0 @ASUS @ASUSTR Woah! Congrats

NVIDIA AI (@NVIDIAAI) — Sun, 18 Oct 2026 00:02:13 GMT

@AlicanKiraz0@ASUS@ASUSTR Woah! Congrats

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Building safer AI agents takes more than model safeguards. @perplexity_ai open sourced SPACE, the a...

NVIDIA AI (@NVIDIAAI) — Sat, 17 Oct 2026 16:04:13 GMT

Building safer AI agents takes more than model safeguards.

@perplexity_ai open sourced SPACE, the agent sandbox platform behind Computer, giving developers more control over where agents run code and what they can access.

Cheers to more teams open-sourcing tools to build safer autonomous AI agents.

Perplexity@perplexity_ai

We're open sourcing SPACE, the sandbox platform that runs every task in Perplexity Computer.

SPACE gives each agent task its own Firecracker microVM and controls credentials and outbound traffic from outside the VM.

Available now under Apache 2.0: github.com/perplexityai/S…

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@ReflectionAI @huggingface And sorry about that @reflection_ai team! Tagged the wrong account.

NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 23:48:31 GMT

@ReflectionAI@huggingface And sorry about that @reflection_ai team! Tagged the wrong account.

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NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 22:03:20 GMT

x.com/ModelScope2022…

ModelScope@ModelScope2022

NVIDIA Nemotron 3.5 Super VL is open released in BF16 and NVFP4: a 120B MoE multimodal agent model that activates just 12B parameters per token. ModelScope’s ms-swift supports both inference and fine-tuning, helping developers put the model to work and adapt it to specialized multimodal applications.
🤖modelscope.ai/collections/nv…8
�github.com/modelscope/ms-…Ms

🏆 Agent performance: Outperforms the 5x larger Nemotron 3 Ultra on the reported Hermes Bench and LangChain evaluations.
⚡ Faster workflows: Delivers up to 5x higher throughput in its size class and completes evaluated agent tasks up to 4x faster.
👁️ Multimodal reasoning: Understands text, images, charts, documents, and video while reasoning, coding, and using tools.
🧰 Built to customize: Fine-tune with your own multimodal datasets using LoRA in ms-swift, or use NVIDIA NeMo for SFT and RL to adapt the model to domain data, tools, policies, and workflows.
🧠 Efficient deployment: NVFP4 with QAD, multi-token prediction, and cross-turn context reuse reduce repeated inference work. Supports vLLM, SGLang, TRT-LLM, and NIM across local, data center, and cloud environments.

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NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 22:03:19 GMT

x.com/actualinc/stat…

Actual Computer@actualinc

Actual Computer turns your NVIDIA DGX Station into a department-scale AI factory.

Read the full case study:
actual.inc/company/blog/c…

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Big shoutout to all of our partners for making Nemotron 3.5 Super VL their own through post-training...

NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 22:03:03 GMT

Big shoutout to all of our partners for making Nemotron 3.5 Super VL their own through post-training 🙌

Here’s what they’ve built 🧵

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Congrats @alexandr_wang and @Muse team on releasing such an epic open weights model!

NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 21:46:49 GMT

Congrats @alexandr_wang  and @Muse team on releasing such an epic open weights model!

Artificial Analysis@ArtificialAnlys

Meta has released Muse Spark Open, an 816B (42B active) open-weights model that scores 39 on the Artificial Analysis Intelligence Index, making it the #1 US open-weights model

Muse Spark Open (xhigh) scores 22 points above Muse Glimmer (high, 17), Meta's 30B dense open-weights model from August. It is also ahead of NVIDIA's Nemotron 3.5 Super VL (26), the next highest-scoring US open-weights model, which launched today

@AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release!

Key Takeaways:

➤ Open weights at the level of Meta's August flagship. Muse Spark Open scores within a point of Muse Spark 1.2 (xhigh, 40). It is stronger on AutomationBench-AA and has a lower hallucination rate, but weaker on GDPval-AA v2.1, CritPt and AA-Omniscience accuracy. Within Meta's current lineup, it sits 13 points behind Muse Wonder (max, 52) and 17 points behind Muse Wonder Full (max, 56)

➤ Token-efficient, at a moderate cost per task. Muse Spark Open uses 40k output tokens per Intelligence Index task, the fewest among the nine highest-scoring open-weights models: e.g. 16% fewer than Muse Spark 1.2 (xhigh, 48k) and 43% fewer than GLM-5.3 (max, 71k), its closest Chinese peer by size. Together AI, Fireworks and Baseten offer it at launch for $1.25/$4.25 per 1M input/output tokens ($0.15 cached input). At that pricing it costs $0.84 per Intelligence Index task. That is less than Muse Spark 1.2 ($0.97) at the same per-token prices and less than half of GLM-5.3 (max, $2.01), but about three times DeepSeek V4.1 Flash (max, $0.27)

➤ A relative weakness in agentic work compared to Chinese open-weights leaders. Among the nine highest-scoring open-weights models, Muse Spark Open ranks last on the four agentic evaluations in the index but fourth on the other six combined, and it leads all open-weights models on AA-Omniscience (25). GLM-5.3 (max), the closest Chinese model by size, owes all of its 6-point lead to the agentic evaluations

➤ Self-hostable on a single node, or two Mac Studios. BF16 weights are ~1.6TB. Meta's NVFP4 checkpoint quantizes only the 256 experts, cutting the weights to ~530GB. Meta says this runs on four Blackwell Ultra GPUs or eight H200s, or on two 512GB M5 Ultra Mac Studios using MLX. The architecture uses 62 layers with grouped-query attention (16 KV heads), 8 of 256 experts active per token, and a 1.8B-parameter Perception Encoder for vision

Other model details:

➤ Parameters: 816B total, 42B active (MoE)

➤ Context window: 1M tokens

➤ License: Muse Model License, a custom license that requires "Built with Muse Spark Open" attribution and a separate agreement with Meta for Model-as-a-Service businesses with over $20M in annual revenue

➤ Input modalities: text, image, video and audio. Output: text

➤ Availability: weights on Hugging Face (BF16 and NVFP4), with API access via Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI

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@ReflectionAI @huggingface Our engineers made it even faster while you were reading! Throughput is a...

NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 17:26:34 GMT

@ReflectionAI@huggingface Our engineers made it even faster while you were reading! Throughput is already up from 70K → ~90K since we posted.

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Welcome Beam! Congrats to @ReflectionAI on launching their first open-weight model. The 501B MoE ...

NVIDIA AI (@NVIDIAAI) — Fri, 16 Oct 2026 13:39:40 GMT

Welcome Beam! 

Congrats to @ReflectionAI on launching their first open-weight model.

The 501B MoE model was trained on NVIDIA GB300 NVL72 and delivers over 70K tokens/sec/GPU on Blackwell Ultra out of the box on day 0.

🤗 Beam 1.0 NVFP4 checkpoint on @huggingface:huggingface.co/reflection/Bea…L

Reflection@reflection_ai

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners.

We're releasing:
- Weights, tokenizer, and reference implementation
- BF16, FP8 and NVFP4 weights and a DFlash2 drafter
- The open source harness we built and trained Beam on: Mirror CLI

Weights and model card on Hugging Face: huggingface.co/reflection

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Can’t stop, won’t stop. The open models keep dropping FLUX 3 Image Klein 5.6B was trained on NVIDIA...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 23:38:49 GMT

Can’t stop, won’t stop. The open models keep dropping

FLUX 3 Image Klein 5.6B was trained on NVIDIA GB200 NVL72 and can run on an RTX GPU.

Big 🙌 to the @bfl_ai team. There's also an NVFP4 variant:huggingface.co/black-forest-l…f

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NemoClaw was just named one of TIME’s Best Inventions of 2026! 🦞 So proud of the team and shoutout...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 22:06:23 GMT

NemoClaw was just named one of TIME’s Best Inventions of 2026! 🦞

So proud of the team and shoutout to everyone building safer AI agents with us.

Blurb on @TIME:time.com/collection/bes…5

TIME@TIME

These are creations changing how we live, work, play, and think about what’s possible. time.com/article/2026/1…

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And this one fits into 24GB of memory. Introducing Nemotron 3.5 Bolt 24GB ⚡️

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 20:04:11 GMT

And this one fits into 24GB of memory.

Introducing Nemotron 3.5 Bolt 24GB ⚡️

NVIDIA RTX Spark@NVIDIARTXSpark

NVIDIA Nemotron 3.5 Bolt 24GB puts powerful local agents within reach. ⚡

74B-A9B parameters packed into 24GB of memory, balancing intelligence and speed while running alongside your apps. Optimized for RTX Spark and ready for your next project.

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And @AIatMeta went live with Muse Spark Open! The 816B model was trained on NVIDIA GB300 NVL72. Its...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 18:24:52 GMT

And @AIatMeta went live with Muse Spark Open!

The 816B model was trained on NVIDIA GB300 NVL72. Its 1M-token context window gives coding agents room to work across large codebases.

Meta also produced NVFP4 checkpoints optimized for Blackwell Ultra, delivering over 50K tokens/sec/GPU to improve throughput and lower cost per token.

Congrats to the @Muse team.

More in our blog: nvda.ws/4B8d9g4
NVFP4: huggingface.co/meta-models/Mu…

AI at Meta@AIatMeta

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date.

Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.

Key Model Specs:
→ 816B total / 42B active parameters
→ 1M token context window
→ Natively multimodal across text, image, video, audio

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And shoutout to "Joe Nemotron" aka @llm_wizard

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 16:11:20 GMT

And shoutout to "Joe Nemotron" aka @llm_wizard

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Nemotron 3.5 Super VL pairs strong reasoning with high output speed, even at 100K input tokens. See ...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 15:09:13 GMT

Nemotron 3.5 Super VL pairs strong reasoning with high output speed, even at 100K input tokens. See how it compares on @ArtificialAnlys 👇

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How we built Nemotron 3.5 Super VL, including its post-training approach, multi-token prediction, an...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 15:09:13 GMT

How we built Nemotron 3.5 Super VL, including its post-training approach, multi-token prediction, and inference optimizations: nvda.ws/3Wqw7i6

As always, NVIDIA Nemotron 3.5 Super VL is open and customizable. This includes weights, data and recipes. Available now on @huggingface → huggingface.co/nvidia/NVIDIA-…

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Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, ima...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 15:09:11 GMT

Meet NVIDIA Nemotron 3.5 Super VL

Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents.

120B total parameters with 12B active 🧵

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Agents may need to read documents, interpret charts, and search the web to complete a task. Nemotro...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 15:09:11 GMT

Agents may need to read documents, interpret charts, and search the web to complete a task.

Nemotron 3.5 Super VL brings text and visual understanding into one model, reducing the need to pass context between separate text and vision models.

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Congrats to StepFun on Step 5 Preview 🙌 Their largest open weights release. The 600B MoE model is ...

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 13:52:20 GMT

Congrats to StepFun on Step 5 Preview 🙌 Their largest open weights release.

The 600B MoE model is built to handle agent tasks lasting up to 24 hours.

The model runs at nearly 8K tokens/sec/GPU on NVIDIA GB300 using NVFP4.

NVP4 checkpoint:huggingface.co/stepfun-ai/Ste…Y

Deploy with @SGL_project -docs.sglang.io/cookbook/autor…0  or @vLLM_project -recipes.vllm.ai/stepfun-ai/Ste…4

Post-train with NeMo Automodel:github.com/NVIDIA-NeMo/Au…k

Run it with NVIDIA NIM using a single command:catalog.ngc.nvidia.com/orgs/nim/nvidi…W

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Last claw stop before Berlin. Good to see you, Stockholm. 🇸🇪🦞

NVIDIA AI (@NVIDIAAI) — Thu, 15 Oct 2026 12:49:11 GMT

Last claw stop before Berlin. 

Good to see you, Stockholm. 🇸🇪🦞

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So many models launching this week! And we have one of our own coming...⌛️

NVIDIA AI (@NVIDIAAI) — Wed, 14 Oct 2026 20:29:17 GMT

So many models launching this week!
And we have one of our own coming...⌛️

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Build-A-Claw London ✅️ A visit from @JensenHuang ✅️ A very good day to have a raffle ticket ✅️ Than...

NVIDIA AI (@NVIDIAAI) — Wed, 14 Oct 2026 19:43:50 GMT

Build-A-Claw London ✅️
A visit from @JensenHuang ✅️
A very good day to have a raffle ticket ✅️

Thank you to everyone who stopped by!

See you in the next city.

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You give an AI agent a task. What happens next? This video walks through how agents use models and ...

NVIDIA AI (@NVIDIAAI) — Wed, 14 Oct 2026 16:23:32 GMT

You give an AI agent a task. What happens next?

This video walks through how agents use models and tools to complete tasks, and what makes them fast and responsive.

No technical background needed.

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European cities are using AI agents to support everyday operations, from managing energy use and mai...

NVIDIA AI (@NVIDIAAI) — Wed, 14 Oct 2026 06:25:56 GMT

European cities are using AI agents to support everyday operations, from managing energy use and maintenance in public buildings to improving services across the city.

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Check out what these cities are doing: https://t.co/WMzBz9XUtt

NVIDIA AI (@NVIDIAAI) — Wed, 14 Oct 2026 06:25:56 GMT

Check out what these cities are doing: blogs.nvidia.com/blog/european-…

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@huggingface We just shared how we post-trained Nemotron for this result, plus IMO gold-level perfor...

NVIDIA AI (@NVIDIAAI) — Tue, 13 Oct 2026 18:56:15 GMT

@huggingface We just shared how we post-trained Nemotron for this result, plus IMO gold-level performance. 

Check out the recipe 👉huggingface.co/blog/nvidia/ne…k

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Customizing Vertical Voice Agents with ASR and LLM Fine-Tuning https://t.co/dBSfkUiKeX

NVIDIA AI (@NVIDIAAI) — Tue, 13 Oct 2026 17:00:15 GMT

Customizing Vertical Voice Agents with ASR and LLM Fine-Tuning x.com/i/broadcasts/1…

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Barcelona showed up ready to build 🦞. The road to #NVIDIAGTC Berlin is officially underway.

NVIDIA AI (@NVIDIAAI) — Tue, 13 Oct 2026 13:41:33 GMT

Barcelona showed up ready to build 🦞.

The road to#NVIDIAGTCC Berlin is officially underway.

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Great to see what the Fin team has built with NVIDIA Nemotron 🙌 They fine-tuned Nemotron 3 Super a...

NVIDIA AI (@NVIDIAAI) — Mon, 12 Oct 2026 18:27:04 GMT

Great to see what the Fin team has built with NVIDIA Nemotron 🙌

They fine-tuned Nemotron 3 Super and Nano for their new Fin Apex customer service models.

Congrats on the launch!

Fergal Reid@fergal_reid

We’re pleased to announce the launch of our 2.0 series of Fin Apex models!

We think these are the best models for customer service.
They are incredibly fast and efficient to run, but even beyond that, we’re seeing an increasing, not shrinking, performance gap between our specialised models, and the general frontier.

We recently did a backtest against Astra, an amazing general model, and you’d expect overkill for customer service - but it still underperforms Apex on our particular tasks.
So, at least in our experience, there’s still a lot of room for domain specific customisation.

We try to give some flavour of the sort of tradeoffs necessary to achieve this performance in the launch post:

fin.ai/blog/announcin…

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How H-Company Optimizes VLM Serving for Computer Use Agents With NVIDIA Dynamo https://t.co/gixR8bJL...

NVIDIA AI (@NVIDIAAI) — Thu, 08 Oct 2026 11:00:14 GMT

How H-Company Optimizes VLM Serving for Computer Use Agents With NVIDIA Dynamo x.com/i/broadcasts/1…

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An AI agent makes a mistake early in a task, then keeps going in the wrong direction. Our researche...

NVIDIA AI (@NVIDIAAI) — Wed, 07 Oct 2026 20:18:52 GMT

An AI agent makes a mistake early in a task, then keeps going in the wrong direction.

Our researchers built PivotOPD to teach agents how to avoid those mistakes and recover when they happen. During training, a teacher model shows the agent a better action and how to get back on track over the next few steps.

Read the paper and watch how it works: research.nvidia.com/labs/lpr/pivot…

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What's New in CUDA 13.4 https://t.co/Vow2yrcQmP

NVIDIA AI (@NVIDIAAI) — Wed, 07 Oct 2026 18:07:58 GMT

What's New in CUDA 13.4 x.com/i/broadcasts/1…

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Great to see SynthID Detector now available to everyone. We use SynthID to watermark content genera...

NVIDIA AI (@NVIDIAAI) — Wed, 07 Oct 2026 17:14:12 GMT

Great to see SynthID Detector now available to everyone.

We use SynthID to watermark content generated by NVIDIA Cosmos on build.nvidia.com. Now, anyone can upload that content to the detector and check that it came from our models.

Google DeepMind@GoogleDeepMind

SynthID Detector is now available to everyone. 🌐

Check whether online content was generated using @GoogleAI, or with tools from our industry partners – including @OpenAI, @NVIDIA, Kakao and coming soon, @Apple.

Try it out →synthid.com6

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DGX Spark Live: Smart Routing for Hybrid AI https://t.co/zhdVIr7dOq

NVIDIA AI (@NVIDIAAI) — Tue, 06 Oct 2026 18:29:32 GMT

DGX Spark Live: Smart Routing for Hybrid AI x.com/i/broadcasts/1…

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Evaluating Open Models with Artificial Analysis | Nemotron Labs https://t.co/PnydgvUsKM

NVIDIA AI (@NVIDIAAI) — Tue, 06 Oct 2026 16:59:49 GMT

Evaluating Open Models with Artificial Analysis | Nemotron Labs x.com/i/broadcasts/1…

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Some telecom network problems don’t have a fix in the runbook. AI agents can investigate, compare f...

NVIDIA AI (@NVIDIAAI) — Tue, 06 Oct 2026 13:12:49 GMT

Some telecom network problems don’t have a fix in the runbook.

AI agents can investigate, compare fixes and test them in simulation. NVIDIA Nemotron helps agents reason through problems, while NVIDIA OpenShell controls what they can access and do.

See how: nvda.ws/4y9vB5x

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In our 2026 telecom AI survey: • 89% of respondents said open source models and software are importa...

NVIDIA AI (@NVIDIAAI) — Tue, 06 Oct 2026 13:12:48 GMT

In our 2026 telecom AI survey:
• 89% of respondents said open source models and software are important to their AI strategy.
• 66% said their company is actively using AI, up from 49% last year.

Read the report: nvda.ws/4rPpi5i

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Telcos need AI agents that understand their networks. We provide the full stack to turn open models...

NVIDIA AI (@NVIDIAAI) — Tue, 06 Oct 2026 13:12:47 GMT

Telcos need AI agents that understand their networks.

We provide the full stack to turn open models into specialized agents while keeping operators in control.

@ATT, @SoftBank and @Indosat are adopting open models for use cases like autonomous network operations and adapting them to local languages and industry needs.

Here’s why they’re choosing open models and how they’re putting them to work. 🧵

Read more:nvda.ws/4dHMAELL

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The model behind this result is now on @huggingface 🤗 Nemotron-Labs-3-Competitive-Coding is a comp...

NVIDIA AI (@NVIDIAAI) — Mon, 05 Oct 2026 20:21:59 GMT

The model behind this result is now on @huggingface 🤗

Nemotron-Labs-3-Competitive-Coding is a competitive-programming specialist model based on Nemotron-3-Ultra.huggingface.co/nvidia/NVIDIA-…0

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Huge thank you to everyone who downloaded Nemotron 3 Diarization and helped it trend on @huggingface...

NVIDIA AI (@NVIDIAAI) — Sat, 03 Oct 2026 00:29:44 GMT

Huge thank you to everyone who downloaded Nemotron 3 Diarization and helped it trend on @huggingface! And we appreciate all the comments.

@sabbassi_11 answered a few of your questions:

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NVIDIA AI@NVIDIAAI

When several people talk at once, a transcript can get messy fast.

Our new Nemotron 3 Diarization model tracks who spoke when, even when voices overlap. It handles up to eight speakers, has 100M parameters, and is now available on @huggingface 🤗

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A speech model can support Arabic and still struggle with local dialects. Fine-tuning NVIDIA Nemotr...

NVIDIA AI (@NVIDIAAI) — Fri, 02 Oct 2026 17:05:27 GMT

A speech model can support Arabic and still struggle with local dialects.

Fine-tuning NVIDIA Nemotron 3.5 ASR cut word error rate on Najdi and Hijazi Saudi Arabic from 55% to 30%.

Check out our new tutorial and learn how to adapt it for other dialects and languages: nvda.ws/4hYqGPM

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Congrats @CoreWeave on RL Rollouts! RL post-training involves a lot of back and forth: train the mo...

NVIDIA AI (@NVIDIAAI) — Fri, 02 Oct 2026 00:34:50 GMT

Congrats @CoreWeave on RL Rollouts!

RL post-training involves a lot of back and forth: train the model, generate responses, then train again. Inference workers need to load the updated model weights each time. As models get bigger, that can leave GPUs waiting.

CoreWeave’s new service uses ModelExpress and Router in NVIDIA Dynamo to speed up those reloads with minimal downtime.

Working with us and @youdotcom, CoreWeave achieved 15× faster model reloads compared with its baseline while post-training Nemotron 3.5 Lightning.

Check out their blog below for details

CoreWeave@CoreWeave

ICYMI: CoreWeave Forge is here 🎉

A production trace that never reaches the next training run is a signal you paid for and threw away. Most teams do it every day, because the tool that catches the trace and the tool that runs the training came from different vendors and were never built to talk.

Forge closes that gap by unifying @wandb, post-training from @OpenPipeAI, and @marimo_io notebooks in one connected environment with CoreWeave Training, Inference, Sandboxes, and Registry.

Run, observe, curate, improve, evaluate. The traces you flag in production become the datasets you train on and the evaluations you gate with.

Open across any model, framework, or cloud.

@MasterClass and @canva are already building on it. 

Free, Pro, and Enterprise available today:crwv.co/utcAwp

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Hey @elonmusk - does this give Ludicrous+ mode a boost?

NVIDIA AI (@NVIDIAAI) — Thu, 01 Oct 2026 16:50:01 GMT

Hey @elonmusk - does this give Ludicrous+ mode a boost?

Paul Merolla@paul_merolla

Only in Palo Alto 😂

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Build Visual AI Agents From a Prompt With NVIDIA Cosmos and VSS 3.3 https://t.co/kEVxL6AoIv

NVIDIA AI (@NVIDIAAI) — Thu, 01 Oct 2026 15:59:17 GMT

Build Visual AI Agents From a Prompt With NVIDIA Cosmos and VSS 3.3 x.com/i/broadcasts/1…

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