Topic
All digests tagged AI Inference
Suggested route
Move from earlier context through the available material to the newest signals. This route is a reading aid, not a difficulty ranking.
Learn how to deploy NVIDIA Dynamo on Kubernetes for scalable AI inference. We cover the Dynamo Operator, DGDR/DGD paths, and the request flow from OpenAI-compatible front ends to engines like vLLM.
Read article
Modular addresses the AI software mess by introducing Mojo, a Pythonic systems language, and MAX, a unified inference framework. This stack enables portable, high-performance deployment across mixed, multi-node hardware.
Read article
CoreWeave details the AI inference landscape, guiding teams through Serverless, Dedicated, and CKS paths. Learn how to deploy custom models at scale with explicit GPU control and managed operations.
Read article
Small open-source models offer massive cost and latency advantages over managed APIs. The solution requires a decentralized architecture using a shared queue, allowing workers to self-batch and double cluster throughput.
Read article
The inference platform supports two models: serverless (pay-per-token) and dedicated (billed per GPU hour). Optimization relies heavily on KV cache awareness and techniques like 4-bit quantization.
Read article
TensorRT Model Connect simplifies the path from open-source models to deployable applications. It provides a consistent workflow to convert models into optimized bundles, supporting everything from LLMs to video generation on multi-device setups.
Read article
Cerebras CTO Sean Lie discusses how ultra-fast inference (up to 10,000 TPS) is enabling new AI capabilities. He details CS4 and the roadmap for CS5, emphasizing wafer scale architecture and heterogeneous system design.
Read article