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KServe serves your ML model on Kubernetes from a few lines of YAM, scaling out under load, to zero when idle, and updating with zero downtime.
Running an open-source LLM on AWS sounds straightforward at first. Pick a model, deploy it, send requests, and you're done. But once you start thinking about production, things bec...
a brave new world
Deploy production-ready Hugging Face models on Amazon SageMaker AI using six open-source agent skills. Point a coding agent at a model and get back a real-time endpoint with the ri...
Learn how to deploy decision models with llama.cpp on Amazon SageMaker AI. This technical how-to serves OpenAI-compatible chat completions with the AWS Deep Learning Container for...
TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the fra...
Prime Intellect has launched Prime Inference, an OpenAI-compatible platform for serving frontier open models on NVIDIA Blackwell. Its GLM-5.3 deployment uses Dynamo, vLLM and NVFP4...
The Foundry model catalog covers a lot of ground, but it doesn’t cover everything. If you need a model trained on your own proprietary…Continue reading on CodeToDeploy »
Enterprises and individual developers frequently run multiple AI inference models. The right architecture can simplify how the models are called while also providing centralized go...
The Foundry model catalog covers a lot of ground, but it doesn't cover everything. If you need a model trained on your own proprietary, tabular data, a churn predictor built on you...
Why AI inference is a system-level workload, and how to balance hardware for production LLMs.Continue reading on Medium »
Turning a trained churn classifier into a FastAPI service that other software can actually call. The post My Model Worked Perfectly. Then I Tried to Make It Useful. appeared first...
Learn how i14 trained and served Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch. Its recurrent state stays fixed at 29.5 MB per sequence regardless of...
A practical framework for choosing the right tools at each layer of your local AI setup, from model serving to context retrieval.
On 28 September Unsloth began serving Laya, an open decision model, at a Jev-compatible endpoint on a CPU. On 7 October it shipped…Continue reading on Medium »
Originally appeared on Ruby on Rails: Compress the complexity of modern web apps.Today we’re expanding Agents on Rails with Stage 2, a new set of benchmark tasks that exercise a mo...
Originally appeared on Shopify Engineering.Sidekick’s frontier model hands its most token-heavy work to open models that Shopify post-trains and serves itself. Here’s the loop we u...
AI applications do not always need the same language model for every request. A simple factual question may only require a small, fast model, while a request involving multiple con...
If you are entering the world of Artificial Intelligence, standard CPU-based servers simply won’t cut it. To handle massive datasets, you…Continue reading on Medium »
Learn how to deploy an available model from Amazon SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint wh...
To deploy an LLM with vLLM, prepare your GPU server and install vLLM there, then load your preferred vLLM-compatible model [...] Read More... The post How to deploy an LLM with vLL...
Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quant...
A trained model sitting in a Jupyter notebook has never made anyone money. The accuracy score can be excellent, and it still won’t matter…Continue reading on Medium »
Building a FastAPI endpoint for churn prediction, and everything that broke between "it runs" and "it's live The post Your Model Isn't Done Until Someone Else Can Call It appeared...
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