Following up on my previous post about Trump announcing the formation of an “AI Force,” we now have the next step: Trump has announced the formation of the Super Intelligence Force (SIF).

According to Trump, the SIF will coordinate the federal government’s efforts around super intelligence and engage with critical infrastructure providers, super intelligence companies, consumers, public interest groups, and religious organizations.

For CoreWeave, the interesting connection is its existing involvement in the Department of Energy’s Genesis Mission, where it is already participating alongside other major AI and infrastructure companies. The DOE officially lists CoreWeave among the 24 organizations that signed collaboration agreements for the initiative.

This doesn’t mean CoreWeave has received any new contract or funding from the SIF, but it reinforces the broader trend of the U.S. government treating AI compute and infrastructure as strategically important.

Previous post: Trump Announces Formation of an AI Force, Plans to Appoint an AI Czar | CoreWeave's Genesis Mission Connection : r/CRWV

The next thing to watch is whether the SIF leads to concrete government funding, procurement, or infrastructure programs that involve companies like CoreWeave.

CoreWeave announced that it has brought up a multi-rack NVIDIA Vera Rubin NVL72 cluster, connecting hundreds of Rubin GPUs into a single scale-out cluster on CoreWeave Cloud to support large-scale agentic AI workloads.

This builds on CoreWeave's earlier milestone as the first AI cloud provider to validate and bring up a single Vera Rubin NVL72 rack.

A few highlights:

• Scaling agentic AI: The multi-rack cluster enables training and inference across hundreds of Rubin GPUs, supporting more demanding inference workloads and reinforcement learning at scale. For multi-step agents, reducing data-access latency is particularly important because delays can accumulate across repeated model calls and tool use.
• Networking: Each NVL72 rack contains 72 Rubin GPUs and 36 Vera CPUs. NVIDIA Spectrum-X Ethernet connects multiple racks into a single system, providing 1.6 Tb/s of scale-out connectivity per GPU. The network architecture is designed to support approximately 128,000 GPUs per rail, providing room for further expansion.
• Deployment automation: CoreWeave Mission Control automates rack setup, firmware updates, validation, power and cooling. Racks undergo full-system testing before entering production.
• Storage improvements: CoreWeave introduced cross-region write acceleration, allowing workloads to write data locally while it is replicated to another region in the background. This reduces delays for training checkpoints and agentic workloads that depend on fast data access. CoreWeave also launched a lower-cost Archive tier with no retrieval fees, early-deletion fees or fees for reading data directly from the Archive tier.

CoreWeave also highlighted its LOTA (Local Object Transport Accelerator), which delivers up to 7 GB/s of throughput per GPU and an 8× reduction in read latency compared with traditional storage clusters.

Chen Goldberg, CoreWeave's executive vice president of product & engineering, said:

“With multi-rack Vera Rubin, we are connecting hundreds of Rubin GPUs as a single scale-out cluster. For customers building agentic AI, that means greater scale, faster iteration, and higher productivity as models and agents continuously learn and improve.”

Official announcement: CoreWeave Brings Up Multi-Rack NVIDIA Vera Rubin NVL72 Cluster

CoreWeave and Parallel Works announced the deployment of a fully managed AI and high-performance computing environment for DARPA, the U.S. Department of Defense’s advanced research agency.

The environment will support DARPA’s NODES (Network of Optimal Dynamic Energy Signatures) program, which uses AI and biological modeling to better understand protein function and help characterize potential biological threats.

The platform is built on CoreWeave’s AI cloud and includes dedicated NVIDIA H100 compute, enterprise-scale storage, InfiniBand networking, CKS and SUNK, and CoreWeave’s observability stack. Parallel Works provides the ACTIVATE control plane and manages the environment for DARPA.

According to CoreWeave, this gives NODES researchers access to the computing resources needed to train AI models and run complex simulations within days instead of spending weeks or months building and managing the infrastructure themselves.

No contract value was disclosed, so I wouldn’t view this as a major financial catalyst by itself.

What I find more interesting is the government/customer diversification angle. Intrator was just talking at Goldman about CoreWeave expanding beyond hyperscalers and AI-native companies into enterprise and government workloads. This looks like a concrete example of that expansion.

It also follows CoreWeave’s collaboration with Leidos around AI infrastructure for U.S. defense, intelligence and national security workloads.

https://www.coreweave.com/news/parallel-works-and-coreweave-to-accelerate-darpa-biological-research-with-fully-managed-ai-cloud-environment

Michael Burry is reportedly covering his CoreWeave short position, according to an update from his Substack.

He opened the CRWV short in August after previously saying he was avoiding the stock because of its limited float and squeeze risk.

Link to post on X

OpenAI launched GPT-6 Astra this week, with a major focus on reasoning, coding, computer use and long-running agentic workloads.

https://x.com/OpenAI/status/2095595741528125780?s=20

For CoreWeave investors, the interesting read-through is less whether Astra itself runs on CoreWeave (there has been no confirmation of that) and more what this model cycle says about compute demand.

CoreWeave and OpenAI already have roughly $22.4B in infrastructure agreements, covering both training and deployment (CoreWeave source). As AI shifts toward agents that can work autonomously for longer periods, inference compute per task could increase substantially.

That could make inference, not just training, an increasingly important part of CoreWeave’s long-term demand story.

OpenAI: GPT-6 Astra announcement and benchmarks

Link: https://x.com/MichaelDell/status/2095984882769866938?s=20

Dell reported another massive quarter for AI infrastructure demand.

In Q2, Dell booked a record $60.9B of AI server orders, generated $16.4B of AI server revenue, and exited with a $95B AI backlog. Even after booking $131.7B of AI server orders over the past 12 months, Dell said its five-quarter pipeline still grew sequentially.

Demand is broadening across neoclouds, sovereign AI and enterprises, with more than 6,500 AI customers. Dell also raised its FY27 AI server revenue outlook from $60B to $74B.

But the most interesting part of the call was Dell’s longer-term view of inference and agentic AI.

Dell Vice Chairman and COO Jeff Clarke said:

> “Inference is past training and is pure demand on our industry.”

Dell expects inference token volume to grow 87x by 2030, while training compute grows around 5x, and expects enterprise agentic AI to become the single largest workload by 2028.

The distinction matters because training is only one stage of the compute cycle. Once models and agents are deployed, every query, reasoning step and tool call creates recurring inference demand.

Dell also expects AI to account for 75% of data-center demand by 2030, requiring roughly 200 GW of additional power, with a significant portion of that buildout coming from neoclouds, sovereign AI and enterprises.

Why this matters for CoreWeave

CoreWeave is not only rapidly expanding GPU capacity. It has also been building out its inference and agentic-AI stack. Its unified agentic capabilities connect training, production inference, observability and reinforcement learning, allowing real-world inference activity to feed back into continuous model improvement.

Its recent guide to deploying Kimi K3 on Dedicated Inference is one concrete example: customers bring their own model weights, while CoreWeave handles the underlying deployment and production serving infrastructure. In CoreWeave’s benchmarks, GB300 delivered 198k tokens/min per GPU at concurrency 32 versus 64k on GB200.

Not all enterprise AI workloads will go through neoclouds; some will remain on-prem. But importantly, Dell says enterprise adoption is expanding alongside continued strong neocloud demand.

Coming shortly after NVIDIA’s earnings, the message looks increasingly consistent:

NVIDIA: compute remains fully utilized and demand would be considerably higher without supply constraints.

Dell: record AI orders and backlog, with its forward pipeline continuing to grow despite $131.7B of AI server orders over the past 12 months, while inference becomes an increasingly important workload.

Both point to the same underlying picture: AI infrastructure still appears constrained more by how quickly compute, power, and data-center capacity can be built than by a lack of demand.

References:

Dell Q2 FY27 Earnings Highlights by @wallstengine on X
Dell Q2 FY27 Prepared Remarks
Dell Q2 FY27 Earnings Call Transcript
CoreWeave: Closing the Training-to-Inference Gap for Autonomous Agents
CoreWeave: Kimi K3 on Dedicated Inference

Following up on the earlier Gavin Baker / Jensen Huang discussion around the broader case for U.S. data-center expansion:

Trump has now also weighed in, arguing that communities should welcome data centers for the jobs, tax revenue and economic development they bring, while framing opposition to data-center expansion as benefiting China.

Related post: Jensen Huang backs the data-center buildout: AI is helping “reindustrialize” America : r/CRWV

This feels particularly relevant after the recent WSJ article covering the growing political opposition to data-center construction, and the pushback from trade unions whose members are benefiting from the buildout.

Gavin Baker’s broader argument is that many of the concerns around data centers, including water use, electricity costs, taxes, jobs and local economic impact, have become more manageable as projects, regulations and infrastructure planning have evolved.

He argues that, on balance, data centers are increasingly benefiting local communities through infrastructure investment, tax revenue and skilled blue-collar jobs, while helping to reindustrialize parts of America that have seen decades of manufacturing decline.

NVIDIA founder and CEO Jensen Huang responded, “Gavin, spot on,” and argued that AI is driving investment across the power grid, energy infrastructure, chip fabs and data centers, while creating construction and manufacturing jobs across the country.

He also wrote, “AI is bringing manufacturing back to America and reindustrializing the nation after decades of offshoring.”

Notably, Jensen has now pinned the post on his X profile.

Another interesting perspective on the infrastructure buildout behind the AI/neocloud boom, especially as the political debate around data-center expansion continues to grow.

Links:

(WSJ article) Blue-Collar Jobs Are the New Flashpoint in Data-Center Fight - WSJ
(Gavin Baker's post on X) https://x.com/GavinSBaker/status/2094081116164460995?s=20
(Jensen Huang's reply on X) https://x.com/JensenHuang/status/2094173025881272408?s=20

“AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue,” said Jensen Huang, founder and CEO of NVIDIA.

NVIDIA’s earnings call had several comments directly relevant to CoreWeave and the broader neocloud/AI infrastructure thesis:

• NVIDIA’s ACIE segment, which includes neoclouds, industrial and enterprise customers, reached $40B in revenue, +25% QoQ and +138% YoY. Growth was driven partly by neocloud capacity additions serving enterprises, AI startups and sovereigns, as well as hyperscalers purchasing neocloud capacity to supplement their own buildouts.

• NVIDIA expects its neocloud partners to exit 2026 with ~8 GW of installed capacity, up from roughly 3 GW at YE2025. NVIDIA also said its compute is fully utilized across every cloud it serves, while demand continues to accelerate despite supply constraints.

• NVIDIA expects non-hyperscaler AI infrastructure, including neoclouds, sovereign AI and enterprise, to represent roughly half of its Data Center business. Sovereign AI, primarily served through regional neoclouds, grew 35% QoQ and more than 3x YoY.

• NVIDIA specifically said it “helped CoreWeave, Nebius and Nscale build entire infrastructure businesses,” and that neoclouds are seeing strong demand pipelines from many diverse offtakers.

• Vera Rubin is now ramping into production. NVIDIA says it delivers 30x higher throughput per MW and 35x lower token cost versus Grace Blackwell Ultra, with purchase orders already received from every major hyperscaler, AI cloud and system OEM.

• NVIDIA said unconstrained demand would be “a lot higher” than its already massive growth outlook, with supply expected to remain a bottleneck through at least FY2028.

• CEO Jensen Huang also said both open and closed/frontier models are simultaneously driving compute demand, with frontier labs generating profitable tokens and being limited primarily by available compute.

The broader message from the call was pretty clear: AI compute demand is still accelerating, NVIDIA compute is fully utilized across every cloud it serves, the neocloud ecosystem is scaling rapidly, and demand is broadening across hyperscalers, enterprises, AI startups and sovereign customers.

For CoreWeave specifically, this comes while it already has 1.5 GW of active power, has Vera Rubin infrastructure running, and has substantial capacity brought online late in Q2 that should contribute more fully to Q3/Q4 revenue.

Rescale is expanding its cloud ecosystem by integrating CoreWeave Cloud for engineering, HPC, and AI workloads.

Through the integration, Rescale customers across industries such as aerospace, automotive, energy, life sciences, manufacturing, and semiconductors will be able to run their workloads on CoreWeave infrastructure.

No contract value was disclosed, so this is more of an ecosystem/distribution partnership than a major contract announcement. Still, the integration gives CoreWeave another channel to attract enterprise workloads and further expands its use cases beyond traditional AI model training.

Source: https://www.coreweave.com/news/rescale-expands-cloud-ecosystem-with-coreweave-for-engineering-and-ai-workloads?utm_campaign=_2026-q3_global_launches_&utm_medium=organic&utm_source=twitter-&utm_content=rescale-pr_pr

Truist previously raised CoreWeave’s PT from $126 to $155 following the exceptional Q2 earnings, citing 25% pricing increases and 5–10% wider margins on recent contracts.

They have now raised it again to $165, expecting higher pricing to increasingly flow through to margins in H2 2026, while shorter-duration deals and re-contracting of prior-generation GPUs could provide additional upside.

Truist continues to maintain a Buy rating on CRWV.

Truist Securities maintains Buy on CoreWeave, raises PT from $126 to $155 : r/CRWV