HPE APAC (@HPE_APAC)
HPE가 NVIDIA와 공동 개발한 턴키 솔루션 'AI Factory'를 소개하는 웨비나 초청 트윗입니다. 주권형 LLM, 문서 자동화 등 산업별 AI 사용 사례를 안전하고 대규모로 운영하는 방법을 다루며, HPE의 AI Factory가 보안·스케일·운영 자동화 측면에서 어떻게 활용되는지 설명합니다.

From sovereign LLMs to document automation, AI use cases are gaining traction across industries. Join our upcoming webinar to explore how to operationalize these workloads securely and at scale with @HPE’s turnkey AI Factory, co-engineered with @NVIDIA: https://t.co/PeyE8VXoh3
Laurence Liang (@LaurenceLiang1)
NVIDIA의 오픈 액세스 자율주행 차량 데이터셋을 시각화하는 사이드 프로젝트 작업을 공유한 트윗입니다. 진행 중인 작업이며 피드백과 권장사항을 받고자 하므로, 데이터셋 탐색·시각화 사례나 자율주행 데이터 활용에 관심 있는 개발자에게 유용한 정보입니다.
Mr Richard@AI+Crypto (@lala_oldtang)
OpenAI가 1,100억 달러(110B) 규모의 자금을 유치했으며 Amazon, Nvidia, SoftBank 등이 참여해 기업가치가 8,400억 달러(840B)로 평가되었다는 소식입니다. 핵심 포인트는 펜타곤과의 협약 체결로, 막대한 자본 유입과 함께 군사·안보 관련 논란이 병행되고 있다는 점입니다.

AI + Crypto Highlights from the Past 24 Hours March 2, 2026 (Morning) OpenAI raised $110B. Amazon, Nvidia, and SoftBank are all in, pushing its valuation to $840B. The key detail? It also signed an agreement with the Pentagon. On one side, there’s capital frenzy; on the other,
Jensen Huang says AI's backlash is "extremely hurtful", and warns it could slow the future of tech.
Is fear killing innovation before it even happens?
History says the answer isn't simple.
🔗 https://geekrealmhub.com/jensen-huang-warns-ai-backlash-could-slow-the-future-of-tech/
Inside P̶a̶l̶a̶n̶t̶ Blume's big beautiful b̶i̶l̶l̶ datacenter.
I bet four big beautiful w̶o̶m̶ elephants can fit in here.
Inference is becoming the primary cost center of AI, and NVIDIA’s Feynman roadmap suggests a shift from training-centric GPUs toward latency-optimized, inference-scale systems.
As real-time agents, copilots, and edge deployments grow, inference sovereignty—where compute is located, how fast it responds, and who controls the hardware—will define the next phase of AI infrastructure.
With NVIDIA GTC 2026 approaching, the key question is whether NVIDIA will formally introduce a new class of inference-focused silicon and fabric to complement its training platforms.
#InferenceSovereignty #LLMInference #AgenticAI #NVIDIA #Feynman #HBM4 #SRAM #AdvancedPackaging #SiliconPhotonics #AIInfrastructure #GPU #GTC2026 #Rubin #Blackwell #DeterministicCompute #LPX #GroqLPU #technology
Inference is becoming the primary cost center of AI, and NVIDIA’s Feynman roadmap suggests a shift from training-centric GPUs toward latency-optimized, inference-scale systems.
As real-time agents, copilots, and edge deployments grow, inference sovereignty—where compute is located, how fast it responds, and who controls the hardware—will define the next phase of AI infrastructure.
With NVIDIA GTC 2026 approaching, the key question is whether NVIDIA will formally introduce a new class of inference-focused silicon and fabric to complement its training platforms.
#InferenceSovereignty #LLMInference #AgenticAI #NVIDIA #Feynman #HBM4 #SRAM #AdvancedPackaging #SiliconPhotonics #AIInfrastructure #GPU #GTC2026 #Rubin #Blackwell #DeterministicCompute #LPX #GroqLPU #technology