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CoreWeave는 NVIDIA Vera Rubin NVL72를 프로덕션 클라우드에 도입합니다.

CoreWeave는 클라우드 플랫폼에서 NVIDIA Vera Rubin NVL72 시스템과 Vera CPU의 가용성을 발표했으며, Cognition은 프로덕션 에이전트 AI 워크로드를 실행하는 최초의 고객입니다.

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Source-provided image accompanying CoreWeave brings NVIDIA Vera Rubin NVL72 to production cloud
기본 소스 문서녹음된 소스
출판사
blogs.nvidia.com
소스 링크
blogs.nvidia.comhttps://blogs.nvidia.com/blog/coreweave-agentic-ai-vera-rubin/
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주요 용어

훈련 후
명령어 튜닝, 선호도 최적화, 안전 튜닝 등 사전 학습 이후 적용되는 학습 단계입니다.
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훈련된 모델이 예측 또는 출력을 생성하는 런타임 단계입니다.
도구 사용
검색, 계산기 또는 API와 같은 외부 도구를 호출하는 모델의 기능입니다.
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무슨 일이 일어났나요?

CoreWeave announced the production availability of NVIDIA Vera Rubin NVL72 systems and Vera CPUs on its cloud infrastructure. Cognition, the developer of the Devin AI software engineer, is the first customer to run production workloads on this new hardware. Additionally, CoreWeave launched CoreWeave Forge, a unified environment for training and evaluating AI models and agents.

CoreWeave announced the availability of NVIDIA Vera Rubin NVL72 systems with Spectrum-X 102.4T Ethernet networking on its cloud platform. This makes CoreWeave one of the first cloud providers to deliver this next-generation AI infrastructure to customers. The announcement was made at the CoreWeave Fully Connected event in San Francisco.

Cognition, the applied AI lab behind the Devin AI software engineer, is the first customer to run production workloads on Vera Rubin. Cognition scaled to thousands of GPUs on CoreWeave in nine months and recently benchmarked Vera Rubin’s performance against a GB200 NVL72 baseline using a real-world software engineering workload.

In early tests, Cognition reported that Vera Rubin NVL72 delivered up to a 4.8x increase in total token throughput for SWE-2 workloads compared to GB200 NVL72. These gains translate to faster real-time code generation and more responsive multistep reasoning for Devin.

CoreWeave also announced the availability of NVIDIA Vera, the first CPU built specifically for AI agents. Vera CPUs are designed to handle the high concurrency and isolation requirements of agentic workloads. CoreWeave’s deployment of Vera puts 128 CPUs and 11,264 cores in a single rack, supporting more than 11,000 concurrent environments.

Additionally, CoreWeave launched CoreWeave Forge, a connected environment for training, evaluating, and improving models and agents. Forge unifies Weights & Biases, expertise from OpenPipe, and the open-source marimo notebook project. It is designed to streamline the continuous improvement loop for AI models and agents.

소스 세부정보: blogs.nvidia.com ↗

왜 중요한가요?

This deployment marks a significant shift in AI infrastructure, moving from generic compute to specialized hardware designed for agentic workloads. The Vera Rubin NVL72 offers substantial performance gains for , while the Vera CPU is optimized for running thousands of isolated agent environments simultaneously. This infrastructure supports the growing demand for low-latency, high-concurrency AI agents that require complex reasoning and , enabling companies to scale agentic applications from prototype to production more efficiently.

The introduction of Vera Rubin NVL72 and Vera CPUs addresses specific bottlenecks in agentic AI, such as the need for low-latency compute and the ability to run thousands of isolated agent environments simultaneously. This specialized infrastructure can significantly reduce the cost and complexity of deploying agentic AI systems.

The 4.8x increase in token throughput reported by Cognition suggests that Vera Rubin NVL72 can handle the high-volume, complex reasoning tasks required by agentic AI more efficiently than previous generations. This could lead to faster development cycles and more responsive AI applications.

CoreWeave Forge aims to solve the fragmentation in AI development tools by providing a unified environment for training, evaluation, and deployment. This can help AI teams iterate more quickly and reduce the signal loss that occurs when moving between different tools and vendors.

The partnership between NVIDIA and CoreWeave highlights the growing importance of co-engineered infrastructure for AI. By working closely with cloud providers, NVIDIA can optimize its hardware and software for specific AI workloads, while CoreWeave can offer differentiated services to its customers.

Interactive Mechanism

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Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
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AI Models Explained Quiz

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다음에 무엇을 볼 것인가

Monitor the adoption of Vera Rubin NVL72 by other major AI labs and enterprises. Watch for further performance benchmarks from Cognition and other early adopters. Observe the expansion of CoreWeave Forge and its integration with other AI tools and frameworks. Track the impact of NVIDIA Vera CPUs on the cost and scalability of agentic AI deployments.

Watch for other major AI labs and enterprises to adopt Vera Rubin NVL72 and Vera CPUs on CoreWeave. The performance and cost benefits reported by Cognition may drive broader adoption.

Monitor further benchmarks and real-world performance data from early adopters of Vera Rubin NVL72 and Vera CPUs. This will provide a clearer picture of the practical benefits of this new infrastructure.

Observe the growth and feature development of CoreWeave Forge. Its ability to integrate with other AI tools and frameworks will be key to its success in the AI development ecosystem.

Track the impact of NVIDIA Vera CPUs on the scalability and cost of agentic AI deployments. The ability to run thousands of isolated environments efficiently could enable new types of agentic AI applications.

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