Intro In the previous post, we explored how the vLLM Production-Stack upgrades vanilla vLLM engine to an enterprise-grade platform. This time, weโll crack open the Helm chart, decoding the key knobs in values.yaml and showing deployment recipes that span from a minimal install to full cloud setups. Acknowledgment: While authored independently, this series benefited from …
Read more “vLLM production-stack: Deployment in the cloud (part2)”
Intro No invite? No quota? No problem. If youโve tried to create an account on CoreWeave, you already know the drill: thereโs No open self-registration, No free tier, and No โSign up with GitHubโโwithout an invite. That’s why I decided to write my first CoreWeave blog post. This post shows how to get started with …
Intro The vLLM Production Stack is designed to work across any cloud provider with Kubernetes. After covering AWS EKS, Azure AKS, and Google Cloud GKE implementations, today we’re deploying vLLM production-stack on Nebius Managed Kubernetes (MK8s) with the same Terraform approach. Nebius AI Cloud is purpose-built for AI/ML workloads, offering cutting-edge GPU options from NVIDIA …
Read more “vLLM Production Stack on Nebius K8s with Terraform๐ง๐ผโ๐”
Intro Welcome back to the terraform vLLM Production Stack series! After covering AWS EKS and Azure AKS, today we’re deploying vLLM production-stack on Google Cloud GKE with the same Terraform approach. This guide shows you how to deploy a production-ready LLM serving environment on Google Cloud, with GCP-specific optimizations including Dataplane V2 (Cilium eBPF), VPC-native …
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Intro The world of LLMs is dominated by one expensive bottleneck: GPU memory. This directly impacts how many models can fit and how fast can they generate text, especially in multi-turn conversations or for processing long contexts. The solution is KV Cache Offloading (i.e with LMCache). One technology was perfectly suited to supercharge it, Intel …
Read more “The link That Never Was: Intel Optane PMem + LLM KV Cache Offload”