# skypilot

Published articles for skypilot.

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

## Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot

DevFeed: [Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot](<https://devfeed.tech/articles/run-ai-workloads-on-any-cloud-store-on-hugging-face-zero-egress-storage-with-skypilot-7475.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/skypilot-hf-storage>)

Author: Nikhil Jha; Zhanghao Wu; Hope Wang; Adrien Carreira; Julien Chaumond

Published: 2026-07-07T00:00:00Z

Content type: article

Language: en

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

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Containers](<https://devfeed.tech/topics/containers.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [containers](<https://devfeed.tech/tags/containers.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-collab](<https://devfeed.tech/tags/open-source-collab.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [s3](<https://devfeed.tech/tags/s3.md>), [skypilot](<https://devfeed.tech/tags/skypilot.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### AI overview

Hugging Face Storage integrates with SkyPilot so models, datasets, and Spaces can be mounted into jobs running on GPUs across clouds, Kubernetes, Slurm, and on-premises infrastructure. The integration offers zero-egress reads, Xet-backed deduplication, filesystem-level fetching, and local caching across the AI workload lifecycle.

### Source excerpt

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

## H Company's new Holo2 model takes the lead in UI Localization

DevFeed: [H Company's new Holo2 model takes the lead in UI Localization](<https://devfeed.tech/articles/h-company-s-new-holo2-model-takes-the-lead-in-ui-localization-7009.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Hcompany/introducing-holo2-235b-a22b>)

Author: Ramzi De Coster; Hamza Benchekroun; Aurélien Lac; Tony Wu; Pierre-Louis Cedoz; Kai Yuan; Mart Bakler; Antoine Bonnet; Aleix Cambray; Ronan Riochet

Published: 2026-02-03T17:40:14Z

Content type: article

Language: en

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

Topics: [Localization (l10n)](<https://devfeed.tech/topics/localization.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [skypilot](<https://devfeed.tech/topics/skypilot.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [company](<https://devfeed.tech/tags/company.md>), [development](<https://devfeed.tech/tags/development.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [model](<https://devfeed.tech/tags/model.md>), [model-development](<https://devfeed.tech/tags/model-development.md>), [models](<https://devfeed.tech/tags/models.md>), [performance](<https://devfeed.tech/tags/performance.md>), [release](<https://devfeed.tech/tags/release.md>), [research](<https://devfeed.tech/tags/research.md>), [scale](<https://devfeed.tech/tags/scale.md>), [skypilot](<https://devfeed.tech/tags/skypilot.md>), [training](<https://devfeed.tech/tags/training.md>), [ui](<https://devfeed.tech/tags/ui.md>)

### AI overview

H Company introduces Holo2-235B-A22B Preview, a research model for localizing UI elements in high-resolution interfaces. Its agentic mode iteratively improves predictions and achieves state-of-the-art results on ScreenSpot-Pro, while SkyPilot supports large-scale training across cloud providers and Kubernetes clusters.

### Source excerpt

Two months since releasing our first batch of Holo2 models, H Company is back with our largest UI localization model yet: Holo2-235B-A22B Preview. This model achieves a new State-of-the-Art (SOTA) record of 78.5% on Screenspot-Pro and 79.0% on OSWorld G. Available on Hugging Face, Holo2-235B-A22B Preview is a research release focused on UI element localization. Agentic Localization High-resolution 4K interfaces are challenging for localization models.

## How Shopify uses SkyPilot to route machine-learning workloads across multi-cloud GPU clusters

DevFeed: [How Shopify uses SkyPilot to route machine-learning workloads across multi-cloud GPU clusters](<https://devfeed.tech/articles/skypilot-at-shopify-multi-cloud-gpus-without-the-pain-1622.md>)

Original publisher: [Read original article](<https://shopify.engineering/skypilot>)

Author: Javier Moreno

Published: 2026-01-26T14:49:55Z

Content type: article

Language: en

Sources: [Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering.md>), [Shopify Engineering - Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering-shopify-engineering.md>)

Topics: [skypilot](<https://devfeed.tech/topics/skypilot.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [InfiniBand](<https://devfeed.tech/topics/infiniband.md>)

Tags: [cloud](<https://devfeed.tech/tags/cloud.md>), [development](<https://devfeed.tech/tags/development.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [infiniband](<https://devfeed.tech/tags/infiniband.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [kubernetes-clusters](<https://devfeed.tech/tags/kubernetes-clusters.md>), [multi-cloud](<https://devfeed.tech/tags/multi-cloud.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [shopify](<https://devfeed.tech/tags/shopify.md>), [skypilot](<https://devfeed.tech/tags/skypilot.md>), [yaml](<https://devfeed.tech/tags/yaml.md>)

### AI overview

Shopify describes using SkyPilot to run machine-learning workloads across existing Kubernetes clusters on multiple clouds. A custom plugin routes jobs based on requested hardware and workload needs, while supporting multi-team management, cost tracking, fair scheduling, and policy enforcement.

### Source excerpt

GPUs are annoying. Shopify uses SkyPilot to make them less so: one YAML file, multiple clouds, clean development ergonomics.

## What I learned from looking at 900 most popular open source AI tools

DevFeed: [What I learned from looking at 900 most popular open source AI tools](<https://devfeed.tech/articles/what-i-learned-from-looking-at-900-most-popular-open-source-ai-tools-31798.md>)

Original publisher: [Read original article](<https://huyenchip.com//2024/03/14/ai-oss.html>)

Author: Chip Huyen

Published: 2024-03-14T00:00:00Z

Content type: article

Language: en

Sources: [Chip Huyen](<https://devfeed.tech/sources/chip-huyen.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Development](<https://devfeed.tech/topics/development.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Application Development](<https://devfeed.tech/topics/application-development.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [github](<https://devfeed.tech/tags/github.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-ai](<https://devfeed.tech/tags/open-source-ai.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [skypilot](<https://devfeed.tech/tags/skypilot.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

The article analyzes 896 popular open-source AI repositories, focusing on 845 software repositories after excluding tutorials and aggregated lists. It organizes the ecosystem around foundation models into infrastructure, model development, and application development, and notes differences between China's open-source ecosystem and Western ecosystems.

### Source excerpt

[Hacker News discussion, LinkedIn discussion, Twitter thread] Update (Feb 2026): The full list of open source AI repos is hosted at Good AI List, updated daily. It's balooned to 15K repos, and you can submit missing repos. You can also find some of them on my cool-llm-repos list on GitHub. Four years ago, I did an analysis of the open source ML ecosystem. Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. Data I searched GitHub using the keywords gpt, llm, and generative ai. If AI feels so overwhelming right now, it's because it is. There are 118K results for gpt alone. To make my life easier, I limited my search to the repos with at least 500 stars. There were 590 results for llm, 531 for gpt, and 38 for generative ai. I also occasionally checked GitHub trending and social media for new repos. After MANY hours, I found 896 repos. Of these, 51 are tutorials (e.g. dair-ai/Prompt-Engineering-Guide) and aggregated lists (e.g. f/awesome-chatgpt-prompts). While these tutorials and lists are helpful, I'm more interested in software. I still include them in the final list, but the analysis is done with the 845 software repositories. It was a painful but rewarding process. It gave me a much better understanding of what people are working on, how incredibly collaborative the open source community is, and just how much China's open source ecosystem diverges from the Western one. The New AI Stack I think of the AI stack as consisting of 3 layers: infrastructure, model development, and application development. Infrastructure At the bottom is the stack is infrastructure, which includes toolings for serving (vllm, NVIDIA's Triton), compute management (skypilot), vector search and database (faiss, milvus, qdrant, lancedb), .... Model development This layer provides toolings for developing models, including frameworks for modeling & training (transformers, pytorch, DeepSpeed), inference optimization

## Mistral 7B

DevFeed: [Mistral 7B](<https://devfeed.tech/articles/mistral-7b-6977.md>)

Original publisher: [Read original article](<https://mistral.ai/news/announcing-mistral-7b/>)

Published: 2023-09-27T08:00:00Z

Content type: release

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [llama](<https://devfeed.tech/topics/llama.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [skypilot](<https://devfeed.tech/topics/skypilot.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Code](<https://devfeed.tech/topics/code.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [code](<https://devfeed.tech/tags/code.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama](<https://devfeed.tech/tags/llama.md>), [math](<https://devfeed.tech/tags/math.md>), [mistral](<https://devfeed.tech/tags/mistral.md>), [model](<https://devfeed.tech/tags/model.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [skypilot](<https://devfeed.tech/tags/skypilot.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Mistral AI announces Mistral 7B, a 7.3-billion-parameter language model released under the Apache 2.0 license. The article reports benchmark results exceeding Llama 2 13B in several areas, describes GQA and SWA for inference efficiency, and outlines local, cloud, and Hugging Face deployment options.

### Source excerpt

The best 7B model to date, Apache 2.0