What Is Vertex AI and Why Use It for LLMs? Vertex AI is Google Cloud’s unified machine learning platform. For LLM workloads, it offers two distinct things: access to Google’s own Gemini models through the Vertex AI API, and infrastructure for deploying any LLM (including open-source models like Llama and Mistral) on managed GPU instances. … Read more
Why Enterprises Deploy Private LLMs Most enterprise discussions about LLMs eventually run into the same wall: the data that would make AI most useful is the data the organisation is least willing to send to a third-party API. Customer records, financial data, legal documents, proprietary research, employee information — the content that lives in enterprise … Read more
Why VRAM Is the Binding Constraint When running an LLM locally or deploying one in production, GPU VRAM is almost always the binding constraint — not compute power, not CPU speed, not disk I/O. The model weights must fit in VRAM to run on the GPU at all. If they do not fit, the inference … Read more
What Is Azure OpenAI Service? Azure OpenAI Service is Microsoft’s enterprise-grade deployment of OpenAI’s models — GPT-4o, GPT-4, GPT-3.5 Turbo, embeddings, DALL-E, and Whisper — hosted within Microsoft Azure’s infrastructure. It gives enterprise customers access to the same models as the standard OpenAI API, but with data residency guarantees, private networking options, Azure Active Directory … Read more
What Is vLLM and Why Does It Matter? vLLM is an open-source inference engine built specifically for serving large language models at high throughput. Developed at UC Berkeley and released in 2023, it introduced PagedAttention — a memory management technique that dramatically improves GPU utilisation during inference by handling the KV cache the way an … Read more
Why Structured Outputs Matter LLMs produce free-form text by default, but most production applications need machine-readable output — a JSON object to store in a database, a specific set of fields to populate a UI, a validated data structure to pass to the next step in a pipeline. When a model produces prose where you … Read more
What Is LLM Routing? LLM routing is the practice of directing each request to the most appropriate model rather than sending every request to the same one. Instead of using a single frontier model for everything, a router classifies incoming requests by complexity, cost sensitivity, or task type and dispatches them accordingly — simple queries … Read more
Why Memory Matters for Agents A language model has no persistent memory between API calls. Every request starts fresh from whatever you put in the context window. For a simple Q&A chatbot, this is fine — each turn is mostly self-contained. But for agents that work on multi-step tasks, handle long-running workflows, or serve the … Read more
What Is AI Red Teaming? AI red teaming is the practice of deliberately trying to make your LLM application behave badly — producing harmful outputs, leaking sensitive information, ignoring its instructions, or being manipulated into doing things it should not. The term comes from military and security practice, where a “red team” plays the adversary … Read more
Why Caching Matters for LLM Applications LLM API calls are expensive and slow compared to almost every other operation in a software stack. A single call to a frontier model can cost between a fraction of a cent and several cents, take 1–5 seconds to complete, and involve significant computational overhead on the provider’s side. … Read more
