# energy\_efficiency

Published articles for energy\_efficiency.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## The full stack behind abundant intelligence

DevFeed: [The full stack behind abundant intelligence](<https://devfeed.tech/articles/the-full-stack-behind-abundant-intelligence-6684.md>)

Original publisher: [Read original article](<https://openai.com/index/the-full-stack-behind-abundant-intelligence>)

Published: 2026-08-25T07:05:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [OpenAI](<https://devfeed.tech/topics/openai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Low-Latency Inference](<https://devfeed.tech/topics/low-latency-inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [company](<https://devfeed.tech/tags/company.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [models](<https://devfeed.tech/tags/models.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [openai](<https://devfeed.tech/tags/openai.md>)

### AI overview

OpenAI describes an integrated compute strategy spanning data centers, chips, models, software, products, and devices. It reports that its custom Jalapeño inference chip achieved higher peak throughput per kilowatt and lower token latency than commercial systems on the InferenceX benchmark using GPT-OSS 120B, while also performing strongly on DeepSeek R1 and Kimi K2.

### Source excerpt

OpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost.

## Improving the speed and energy-efficiency of AI agents

DevFeed: [Improving the speed and energy-efficiency of AI agents](<https://devfeed.tech/articles/improving-the-speed-and-energy-efficiency-of-ai-agents-37959.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/improving-ai-agent-speed-and-energy-efficiency-0625>)

Author: Adam Zewe | MIT News

Published: 2026-06-25T04:00:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [agentic workflows](<https://devfeed.tech/topics/agentic-workflows.md>), [Loop Engineering](<https://devfeed.tech/topics/loop-engineering.md>), [Green Software](<https://devfeed.tech/topics/green-software.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [microsoft-azure](<https://devfeed.tech/topics/microsoft-azure.md>)

Tags: [adam-belay](<https://devfeed.tech/tags/adam-belay.md>), [agentic-workflows](<https://devfeed.tech/tags/agentic-workflows.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [computer-science-and-artificial-intelligence-laboratory-csail](<https://devfeed.tech/tags/computer-science-and-artificial-intelligence-laboratory-csail.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [data](<https://devfeed.tech/tags/data.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [defense-advanced-research-projects-agency-darpa](<https://devfeed.tech/tags/defense-advanced-research-projects-agency-darpa.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [electrical-engineering-and-computer-science-eecs](<https://devfeed.tech/tags/electrical-engineering-and-computer-science-eecs.md>), [electronics](<https://devfeed.tech/tags/electronics.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [gohar-chaudhry](<https://devfeed.tech/tags/gohar-chaudhry.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [microsoft-azure](<https://devfeed.tech/tags/microsoft-azure.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [murakkab](<https://devfeed.tech/tags/murakkab.md>), [research](<https://devfeed.tech/tags/research.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [software](<https://devfeed.tech/tags/software.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [sustainable-computing](<https://devfeed.tech/tags/sustainable-computing.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

MIT and Microsoft researchers developed Murakkab, a system that automatically designs and deploys agentic workflows by selecting models, tools, hardware configurations, and computational resources according to user priorities. Tests found that it reduced computational requirements, energy use, and costs without reducing performance.

### Source excerpt

A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.

## New chip could help tiny robots traverse complex environments

DevFeed: [New chip could help tiny robots traverse complex environments](<https://devfeed.tech/articles/new-chip-could-help-tiny-robots-traverse-complex-environments-37974.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/new-chip-could-help-tiny-robots-traverse-complex-environments-0623>)

Author: Adam Zewe | MIT News

Published: 2026-06-23T04:00:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Hardware](<https://devfeed.tech/topics/hardware.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [3D](<https://devfeed.tech/topics/3d.md>), [navigation](<https://devfeed.tech/topics/navigation.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Green Software](<https://devfeed.tech/topics/green-software.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [aeronautical-and-astronautical-engineering](<https://devfeed.tech/tags/aeronautical-and-astronautical-engineering.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [assembly](<https://devfeed.tech/tags/assembly.md>), [augmented-and-virtual-reality](<https://devfeed.tech/tags/augmented-and-virtual-reality.md>), [autonomous-vehicles](<https://devfeed.tech/tags/autonomous-vehicles.md>), [computer-chips](<https://devfeed.tech/tags/computer-chips.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [electrical-engineering-and-computer-science-eecs](<https://devfeed.tech/tags/electrical-engineering-and-computer-science-eecs.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [gleanmer](<https://devfeed.tech/tags/gleanmer.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inside](<https://devfeed.tech/tags/inside.md>), [laboratory-for-information-and-decision-systems-lids](<https://devfeed.tech/tags/laboratory-for-information-and-decision-systems-lids.md>), [low-power](<https://devfeed.tech/tags/low-power.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [national-science-foundation-nsf](<https://devfeed.tech/tags/national-science-foundation-nsf.md>), [navigation](<https://devfeed.tech/tags/navigation.md>), [peter-zhi-xuan-li](<https://devfeed.tech/tags/peter-zhi-xuan-li.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [research](<https://devfeed.tech/tags/research.md>), [research-laboratory-of-electronics](<https://devfeed.tech/tags/research-laboratory-of-electronics.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [sertac-karaman](<https://devfeed.tech/tags/sertac-karaman.md>), [system-on-a-chip](<https://devfeed.tech/tags/system-on-a-chip.md>), [trajectory-planning](<https://devfeed.tech/tags/trajectory-planning.md>), [vivienne-sze](<https://devfeed.tech/tags/vivienne-sze.md>), [zih-sing-fu](<https://devfeed.tech/tags/zih-sing-fu.md>)

### AI overview

MIT researchers developed a low-power chip that combines an efficient mapping algorithm with dedicated hardware to generate detailed 3D maps for robot navigation in real time. The system-on-a-chip uses about 6 milliwatts and is intended for tiny autonomous robots and other battery-limited devices.

### Source excerpt

Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.

## A low-carbon computing platform from your retired phones

DevFeed: [A low-carbon computing platform from your retired phones](<https://devfeed.tech/articles/a-low-carbon-computing-platform-from-your-retired-phones-6739.md>)

Original publisher: [Read original article](<https://research.google/blog/a-low-carbon-computing-platform-from-your-retired-phones/>)

Published: 2026-06-12T17:37:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Green Software](<https://devfeed.tech/topics/green-software.md>), [cloud-computing](<https://devfeed.tech/topics/cloud-computing.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [climate-sustainability](<https://devfeed.tech/tags/climate-sustainability.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [compute](<https://devfeed.tech/tags/compute.md>), [datacenter](<https://devfeed.tech/tags/datacenter.md>), [distributed-systems-parallel-computing](<https://devfeed.tech/tags/distributed-systems-parallel-computing.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [google](<https://devfeed.tech/tags/google.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [servers](<https://devfeed.tech/tags/servers.md>), [smartphones](<https://devfeed.tech/tags/smartphones.md>), [storage](<https://devfeed.tech/tags/storage.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>)

### AI overview

Researchers at the University of California San Diego, with Google's support, are developing a low-carbon cloud computing platform from retired smartphones. A planned datacenter using 2,000 Pixel smartphones aims to provide low-cost computing while reducing the need for newly manufactured hardware and its associated emissions.

### Source excerpt

Climate & Sustainability

## Graviton5's improved design increases speed and energy efficiency -- beyond Moore's law

DevFeed: [Graviton5's improved design increases speed and energy efficiency -- beyond Moore's law](<https://devfeed.tech/articles/graviton5-s-improved-design-increases-speed-and-energy-efficiency-beyond-moore-s-law-7599.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/graviton5s-improved-design-increases-speed-and-energy-efficiency-beyond-moores-law>)

Author: Ali Saidi

Published: 2026-06-10T15:00:00Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [cpu](<https://devfeed.tech/topics/cpu.md>), [Amazon EC2](<https://devfeed.tech/topics/amazon-ec2.md>)

Tags: [amazon-elastic-compute](<https://devfeed.tech/tags/amazon-elastic-compute.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [cache](<https://devfeed.tech/tags/cache.md>), [chip-design](<https://devfeed.tech/tags/chip-design.md>), [cloud-and-systems](<https://devfeed.tech/tags/cloud-and-systems.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [design](<https://devfeed.tech/tags/design.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [formal-verification](<https://devfeed.tech/tags/formal-verification.md>), [graviton](<https://devfeed.tech/tags/graviton.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [speed](<https://devfeed.tech/tags/speed.md>)

### AI overview

Amazon describes Graviton5 CPU and M9g/M9gd EC2 instances, highlighting more cores, faster memory and interconnects, improved branch prediction, and expanded cache capacity.

### Source excerpt

A new chiplet architecture, custom die-to-die connectivity, and support for DDR5-8800 memory and the latest PCIe gen6 interconnects improve performance by 25% for general-purpose and agentic AI workloads.

## Startup's nuclear-inspired cooling system could make data centers more sustainable

DevFeed: [Startup's nuclear-inspired cooling system could make data centers more sustainable](<https://devfeed.tech/articles/startup-s-nuclear-inspired-cooling-system-could-make-data-centers-more-sustainable-37977.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/nuclear-inspired-cooling-system-ferveret-could-make-data-centers-more-sustainable-0610>)

Author: Zach Winn | MIT News

Published: 2026-06-10T04:00:00Z

Content type: article

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [data centers](<https://devfeed.tech/topics/data-centers.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-data-centers](<https://devfeed.tech/tags/ai-data-centers.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [cooling](<https://devfeed.tech/tags/cooling.md>), [data-center-cooling](<https://devfeed.tech/tags/data-center-cooling.md>), [data-center-sustainability](<https://devfeed.tech/tags/data-center-sustainability.md>), [data-centers](<https://devfeed.tech/tags/data-centers.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [energy-storage](<https://devfeed.tech/tags/energy-storage.md>), [ferveret](<https://devfeed.tech/tags/ferveret.md>), [matteo-bucci](<https://devfeed.tech/tags/matteo-bucci.md>), [nuclear-science-and-engineering](<https://devfeed.tech/tags/nuclear-science-and-engineering.md>), [power](<https://devfeed.tech/tags/power.md>), [reza-azizian](<https://devfeed.tech/tags/reza-azizian.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [startups](<https://devfeed.tech/tags/startups.md>), [sustainability](<https://devfeed.tech/tags/sustainability.md>), [sustainable-computing](<https://devfeed.tech/tags/sustainable-computing.md>), [technology-and-society](<https://devfeed.tech/tags/technology-and-society.md>), [water](<https://devfeed.tech/tags/water.md>)

### AI overview

Ferveret, a startup founded by two MIT researchers, is adapting nuclear-reactor heat-transfer methods to cool AI data-center servers with a specialized liquid. Its Adaptive Phase Cooling system uses small, frequently detaching bubbles to improve heat transfer without water and with less electricity.

### Source excerpt

Founded by two researchers from MIT, Ferveret reduces the amount of energy and water required to cool the chips that power AI.

## How ClickHouse Cloud uses AWS Graviton to boost performance and efficiency

DevFeed: [How ClickHouse Cloud uses AWS Graviton to boost performance and efficiency](<https://devfeed.tech/articles/how-clickhouse-cloud-uses-aws-graviton-to-boost-performance-and-efficiency-5269.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/graviton-boosts-clickhouse-cloud-performance>)

Author: Kaushik Iska & Francesco Ciocchetti

Published: 2025-01-27T00:00:00Z

Content type: article

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [migration](<https://devfeed.tech/topics/migration.md>), [ci](<https://devfeed.tech/topics/ci.md>)

Tags: [android](<https://devfeed.tech/tags/android.md>), [apple](<https://devfeed.tech/tags/apple.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [aws](<https://devfeed.tech/tags/aws.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [ci](<https://devfeed.tech/tags/ci.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [database](<https://devfeed.tech/tags/database.md>), [deep-dive](<https://devfeed.tech/tags/deep-dive.md>), [developer](<https://devfeed.tech/tags/developer.md>), [developer-experience](<https://devfeed.tech/tags/developer-experience.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [graviton](<https://devfeed.tech/tags/graviton.md>), [migration](<https://devfeed.tech/tags/migration.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>)

### AI overview

ClickHouse describes migrating ClickHouse Cloud to AWS Graviton ARM processors, covering its AArch64 support, CI and performance work, and the resulting performance, cost, and energy-efficiency goals.

### Source excerpt

We describe our migration from traditional processors to AWS Graviton ARM architecture, detailing the technical challenges and performance gains achieved in optimizing ClickHouse's open-source OLAP database system.

## CO₂ Emissions and Models Performance: Insights from the Open LLM Leaderboard

DevFeed: [CO₂ Emissions and Models Performance: Insights from the Open LLM Leaderboard](<https://devfeed.tech/articles/co2-emissions-and-models-performance-insights-from-the-open-llm-leaderboard-7316.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/leaderboard-emissions-analysis>)

Author: Alina Lozovskaya; Nathan Habib; Albert Villanova del Moral; Clémentine Fourrier

Published: 2025-01-09T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [open-llm-leaderboard](<https://devfeed.tech/topics/open-llm-leaderboard.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [architectures](<https://devfeed.tech/tags/architectures.md>), [energy](<https://devfeed.tech/tags/energy.md>), [energy-efficiency](<https://devfeed.tech/tags/energy-efficiency.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [llm](<https://devfeed.tech/tags/llm.md>), [models](<https://devfeed.tech/tags/models.md>), [open-llm-leaderboard](<https://devfeed.tech/tags/open-llm-leaderboard.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>)

### AI overview

The article analyzes CO₂ emissions from evaluating large language models on the Open LLM Leaderboard. It explains a heuristic based on evaluation time, hardware energy use, and electricity carbon intensity, then examines emissions and performance trends across 2,742 models and several model families.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.