#accelerators-gpus
#accelerators-gpus
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#nvidia
Venture
from24/7 Wall St.
3 days agoNVIDIA Chips are Going to Space. Could the Stock Be Next?
Nvidia is developing space-based data centers, leveraging orbital computing for AI and 5G connectivity as the corporate space race intensifies.
Business
from24/7 Wall St.
4 days agoNvidia Price Prediction: 1 Year Bull and Bear Case
NVIDIA's price target is $237.71, indicating a 34.85% upside despite a recent pullback in shares.
#ai
Python
fromPycon
6 days agoPython and the Future of AI: Agents, Inference, and Edge AI
AI tools are increasingly integrated into development, with a dedicated track at PyCon US focusing on their future and practical applications.
Software development
fromDevOps.com
3 days agoZencoder Adds OpenClaw Alternative to AI Coding Portfolio - DevOps.com
Zencoder's Zenflow Work automates various developer tasks, enhancing efficiency beyond just code generation.
#ai-infrastructure
Venture
fromTechCrunch
1 month agoThinking Machines Lab inks massive compute deal with Nvidia | TechCrunch
Mira Murati's Thinking Machines Lab signed a multi-year strategic partnership with Nvidia involving at least one gigawatt of Vera Rubin systems deployment starting in 2027, with Nvidia also making a strategic investment in the $12 billion-valued AI research company.
Silicon Valley
fromMetro Silicon Valley | Silicon Valley's Leading Weekly
3 weeks agoNvidia GTC in San Jose Highlights AI Boom & Data Center Growth
Nvidia's major tech conference brought significant economic activity to downtown San Jose through robot demonstrations, pop-up installations, and increased restaurant and retail business.
Data science
fromTechRepublic
1 month agoInside the Gas Engine Strategy Powering AI's Next Wave
Gas reciprocating engines are emerging as a critical power solution for AI data centers, with manufacturers like Caterpillar securing multi-gigawatt orders to meet demand that exceeds grid and turbine capacity.
Tech industry
fromTheregister
3 weeks agoA closer look at Nvidia's Groq-powered LPX rack systems
Nvidia acquired Groq for $20 billion primarily to accelerate time-to-market for SRAM-heavy inference chips rather than develop the technology independently, enabling faster token generation for AI reasoning workloads.
Artificial intelligence
fromTechCrunch
3 weeks agoNiv-AI exits stealth to wring more power performance out of GPUs | TechCrunch
AI data centers waste significant power due to GPU demand surges, forcing operators to throttle performance by up to 30%, prompting startups like Niv-AI to develop precision power management solutions.
Artificial intelligence
fromInfoWorld
1 month agoNvidia launches Nemotron 3 Super to power enterprise AI agents
Nemotron 3 Super's hybrid architecture combining Mamba and Transformer technologies enables enterprises to run complex AI agents more efficiently with lower costs and faster execution on existing infrastructure.
Artificial intelligence
from24/7 Wall St.
1 month agoNVIDIA Cements Its Role as the Backbone of AI Infrastructure
NVIDIA's networking revenue grew 162% year-over-year to $8.2 billion, nearly tripling GPU growth, signaling a shift from chip seller to integrated infrastructure provider selling complete AI data center systems.
fromTechCrunch
2 months agoQuadric rides the shift from cloud AI to on-device inference - and it's paying off | TechCrunch
The company, which is based in San Francisco and has an office in Pune, India, is targeting up to $35 million this year as it builds a royalty-driven on-device AI business. That growth has buoyed the company, which now has post-money valuation of between $270 million and $300 million, up from around $100 million in its 2022 Series B, Kheterpal said.
Artificial intelligence
fromInfoQ
2 months agoNVIDIA Dynamo Planner Brings SLO-Driven Automation to Multi-Node LLM Inference
The new capabilities center on two integrated components: the Dynamo Planner Profiler and the SLO-based Dynamo Planner. These tools work together to solve the "rate matching" challenge in disaggregated serving. The teams use this term when they split inference workloads. They separate prefill operations, which process the input context, from decode operations that generate output tokens. These tasks run on different GPU pools. Without the right tools, teams spend a lot of time determining the optimal GPU allocation for these phases.
Artificial intelligence
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