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AI deployment

55 items

ARTICLEDEV.to AI·5/10/2026

How To Select an Enterprise LLM

The article discusses the intensifying competition in enterprise LLM deployment, highlighting new models from OpenAI and Mistral AI. It emphasizes the need for a systematic benchmarking approach that considers latency, cost, and task-specific performance, urging organizations to use a multi-phase evaluation framework to align models with business objectives.

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ARTICLEDEV.to AI·4/20/2026

Beyond the Basics: Real-World BRAG Agent Deployment That Actually Works

This content explores the challenges of deploying AI (BRAG) agents in real-world production, where agents often fail despite local success. The author shares experiences from 47 deployments, noting that 37 failed spectacularly due to issues like agents getting stuck or memory crashes, emphasizing the unique complexities compared to traditional web applications.

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DOCDEV.to AI·25d ago

How to Deploy Mistral Nemo with vLLM + Flash Attention on a $12/Month DigitalOcean GPU Droplet: 3x Faster Inference at 1/95th Claude Cost

This article details how to deploy the Mistral Nemo model on a $12/month DigitalOcean GPU Droplet, leveraging vLLM and Flash Attention. This approach offers 3x faster inference and a 95% cost reduction compared to commercial AI APIs like Claude, advocating for efficient self-hosting of open-source AI models.

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RESEARCHarXiv CS.AI·29d ago

CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment

This paper introduces Deployment-Time Learning (DTL) as a new stage for LLMs, allowing them to continually adapt from experience post-training without modifying core parameters. It presents CASCADE, a framework that uses an explicit, evolving episodic memory for LLM agents, formalizing experience reuse as a contextual bandit problem with no-regret guarantees.

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DOCDEV.to AI·9d ago

How to Deploy Llama 3.2 with Ollama + Kubernetes on a $8/Month DigitalOcean Droplet: Production-Grade Multi-Node Inference at 1/150th Claude Cost

The content details how to deploy a Llama 3.2 inference cluster using Ollama and Kubernetes on an $8/month DigitalOcean Droplet. This guide aims to provide a cost-effective alternative to commercial AI APIs, enabling production-grade multi-node inference with better latency and zero rate limits.

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DOCDEV.to AI·14d ago

How to Deploy Llama 3.2 90B with vLLM + Quantization on a $20/Month DigitalOcean GPU Droplet: Enterprise Reasoning at 1/140th Claude Opus Cost

This content provides a guide on deploying the Llama 3.2 90B model using vLLM and quantization on a DigitalOcean GPU droplet, costing only $20/month. This setup offers enterprise-grade reasoning capabilities at a cost 25 times lower than Claude Opus, achieving significant cost savings for AI infrastructure.

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