Updates and recent posts about vLLM..

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@bergerx started using tool Python , 3 weeks, 2 days ago.

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@bergerx started using tool Kubernetes Dashboard , 3 weeks, 2 days ago.

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@bergerx started using tool Kubernetes , 3 weeks, 2 days ago.

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@bergerx started using tool Kubectl , 3 weeks, 2 days ago.

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@bergerx started using tool Kubeadm , 3 weeks, 2 days ago.

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@bergerx started using tool Go , 3 weeks, 2 days ago.

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@faun shared a link, 3 weeks, 6 days ago

Advanced PostgreSQL Indexing: Multi-Key Queries and Performance Optimization

Advanced PostgreSQL tuning gets real results: composite indexes and CTEs can cut query latency hard when slicing huge datasets. AddLATERALjoins and indexed subqueries into the mix, and you’ve got a top-N query pattern that holds up—even when hammering long ID lists...

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@faun shared a link, 3 weeks, 6 days ago

OpenAI Agent Builder: A Complete Guide to Building AI Workflows Without Code

OpenAI’sAgent Builderdrops the guardrails. It’s a no-code, drag-and-drop playground for building, testing, and shipping AI workflows - logic flows straight from your brain to the screen. Tweak interfaces inWidget Studio. Plug into real systems with theAgents SDK. Just one catch: it’s locked behind P..

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@faun shared a link, 3 weeks, 6 days ago

walrus: ingesting data at memory speeds

Walrusis a lock-free, single-nodeWrite Ahead Log in Rustthat rips through a million ops/sec and moves 1 GB/s of write bandwidth - on bare-metal, nothing fancy. It leans on mmap-backed sparse files, atomic counters, and zero-copy reads to get there. Each topic gets its own line of 10MB memory-mapped ..

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@faun shared a link, 3 weeks, 6 days ago

Inside Husky’s query engine: Real-time access to 100 trillion events

SteamPipe just gutted its real-time storage engine and rebuilt it inRust. Expect faster performance and better scaling. Now runs oncolumnar storage, ships withvectorized queries, and rolls anobject store-backed WAL. Serious firepower for time series data. System shift:Another sign that high-throughp..

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.