A transformer layer is built from familiar operations: linear projections, attention, normalization, activation functions, and residual additions. A direct implementation may launch a separate accelerator kernel for every operation and write every intermediate tensor to device memory. The mathematics is correct, but the execution can spend more time moving values and launching kernels than performing […]
In this article, you will learn how to get reliable structured outputs from large language models by validating them against a contract and the source text, retrying only when it helps, and constraining generation.
In this article, you will learn how to sandbox the code an AI agent writes by running it in a limited subprocess, a hardened Docker container, and a Firecracker microVM, and how to compare their safety and latency.
In this article, you will learn how diffusion language models generate text by iteratively refining a masked sequence, and how this differs from the token-by-token generation of GPT-style models.
Two transformer models with the same parameter count can have very different memory requirements during inference. A major reason is the structure of their attention layers. The number of key-value heads, the representation stored for each token, and the number of earlier tokens retained by each layer determine how much KV cache a request needs. […]
In this article, you will learn how to build a side-by-side benchmark of local and cloud LLM classifiers using Scikit-Ollama and a Groq-backed Scikit-LLM pipeline.
A model is a collection of numbers. The format used to store and compute with those numbers affects how much memory the model occupies, how quickly the hardware can move the numbers, and which arithmetic units can process them. Changing precision can therefore make inference faster without changing the model architecture. Lower precision is not […]
In this article, you will learn what DiffusionGemma is, how its diffusion-based text generation differs from standard autoregressive language models, and how to run it with Hugging Face Transformers and vLLM.
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In this article, you will learn how Python decorators work under the hood and how to build reliable ones for timing, retries, caching, and input validation.
