Intro last year, I have dived deep into Ollama inference where I ended up building and speaking about Ollama Kubernetes deployments along with rich documentation in my ollama_lab repo and quantization article—This year’s Cloudtrhill focus is VLLM Inference which is a next level beast from a model serving standpoint. Exploring multiple inference options is time-intensive …
Intro Tired of sharing your IP & sensitive data to OpenAI ? What if you could run your own private AI chatbot powered by Local Inference & LLMs, with 100% data privacy—all inside a Kubernetes cluster?Today we’ll show you how to deploy an end-to-end LLM inference setup on a Civo Cloud Talos K8s cluster with …
Read more “Ollama deployment on Civo K8s Cluster with terraform”
Intro Too often, machine learning concepts are explained like a mathematician talking to other mathematicians—leaving the rest of us scratching our heads. One of those is kv_cache, a key technique that makes large language models run faster and more efficient.This blog is my attempt to break it down simply, without drowning in dark math :). …
Read more “kv_cache Explained: How It Enhances vLLM Inference”
Intro You’ve probably fired up ollama run some-cool-model tons of times, effortlessly pulling models from Ollama’s Repo or even directly from Hugging Face. But have you ever wondered how those CPU-friendly GGUF quantized models actually land on places like Hugging Face in the first place? What if I told you, you could contribute back with tools you might already be …
Intro Over the past year, I was drowning into GitHub PRs, half-baked redit discussions, videos, and scattered docs trying to decode the chaos of quantization for Large Language Models (LLMs). Everyone was talking about running Llama models on a laptop, but no one was explaining how it actually worked—and forget about finding proper research papers …