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ContentUpdates and recent posts about vLLM..
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@varbear added a new tool npm , 1 month, 2 weeks ago.
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@devopslinks added a new tool GitHub , 1 month, 2 weeks ago.
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@laura_garcia shared a post, 1 month, 2 weeks ago
Software Developer, RELIANOID

𝘐𝘯 𝘤𝘢𝘴𝘦 𝘺𝘰𝘶 𝘮𝘪𝘴𝘴𝘦𝘥 𝘪𝘵: Europe’s skies disrupted

Cyberattack on Collins Aerospace’s MUSE platform We shared this analysis a few months ago, but given the relevance of the topic and the growing impact of cyberattacks on critical infrastructure, it’s definitely worth resurfacing. The incident forced major airports like Heathrow, Brussels, and Berlin..

News FAUN.dev() Team
@kala shared an update, 1 month, 2 weeks ago
FAUN.dev()

DeepSeekMath-V2 Launches with 685B Parameters - Dominates Math Contests

DeepSeekMath-V2

DeepSeekMath-V2, an AI model with 685 billion parameters, excels in mathematical reasoning and achieves top scores in major competitions, now available open source for research and commercial use.

DeepSeekMath-V2 Launches with 685B Parameters - Dominates Math Contests
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@anjali shared a link, 1 month, 2 weeks ago
Customer Marketing Manager, Last9

9 Monitoring Tools That Deliver AI-Native Anomaly Detection

A technical guide comparing nine observability platforms built to detect anomalies and support modern AI-driven workflows.

anamoly_detection
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@kala added a new tool DeepSeekMath-V2 , 1 month, 2 weeks ago.
News FAUN.dev() Team
@kala shared an update, 1 month, 2 weeks ago
FAUN.dev()

A New Challenger: INTELLECT-3's 100B Parameters Punch Above Their Weight

Ansible Lustre Slurm INTELLECT-3

INTELLECT-3, a 100B+ parameter model, sets new benchmarks in AI, with open-sourced training components to foster research in reinforcement learning.

A New Challenger: INTELLECT-3's 100B Parameters Punch Above Their Weight
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@kala added a new tool INTELLECT-3 , 1 month, 2 weeks ago.
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@devopslinks added a new tool Lustre , 1 month, 2 weeks ago.
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@varbear added a new tool Slurm , 1 month, 2 weeks ago.
vLLM is an advanced open-source framework for serving and running large language models efficiently at scale. Developed by researchers and engineers from UC Berkeley and adopted widely across the AI industry, vLLM focuses on optimizing inference performance through its innovative PagedAttention mechanism — a memory management system that enables near-zero waste in GPU memory utilization. It supports model parallelism, continuous batching, tensor parallelism, and dynamic batching across GPUs, making it ideal for real-world deployment of foundation models. vLLM integrates seamlessly with Hugging Face Transformers, OpenAI-compatible APIs, and popular orchestration tools like Ray Serve and Kubernetes. Its design allows developers and enterprises to host LLMs with reduced latency, lower hardware costs, and increased throughput, powering everything from chatbots to enterprise-scale AI services.