The AI Ascend blog.

Jun 04, 2026

Four Bits Per Weight: How Low-Precision Quantization Stopped Hurting LLMs

A 70B model in FP16 needs 140 GB of memory it spends most of its time waiting to read. Dropping each weight to four bits cuts that to 35 GB, and for years that cut also broke the model. Here is what changed.

22 min read

Jun 04, 2026

PagedAttention and Continuous Batching: How vLLM Stopped Wasting Your GPU

A GPU loaded with a 13B model can have most of its KV-cache memory sitting idle while requests queue for capacity. PagedAttention and continuous batching reclaim that memory, and the throughput follows.

21 min read

Jun 04, 2026

Speculative Decoding: How a Small Draft Model Makes Large Language Models Think Faster

Autoregressive decoding forces even the largest language models to emit one token at a time, bottlenecked by memory bandwidth rather than compute. Speculative decoding breaks this sequential constraint by letting a lightweight draft model propose multiple tokens that the target model verifies in a single forward pass, delivering 2-5x speedups with a mathematical guarantee of identical output quality.

24 min read

Jun 03, 2026

Test-Time Compute: How Reasoning Models Buy Intelligence by the Token

A 7B model that thinks for thirty seconds can outscore a 70B model that answers instantly. Test-time compute is the lever that makes this possible, and it is quietly rewriting the economics of model design.

18 min read

Jun 03, 2026

Claude Certified Architect - Foundations: The Complete Exam Preparation Guide

A comprehensive, interactive study guide covering every domain of the Claude Certified Architect - Foundations (CCAF) exam. Master agentic architecture, tool design, Claude Code configuration, prompt engineering, and context management with visual diagrams, worked examples, practice questions, and built-in progress tracking.

120 min read

Jun 03, 2026

From Nano Banana to Vision Banana: How Google DeepMind Turned an Image Generator into a Generalist Vision System

Training a model to generate photorealistic images teaches it geometry, semantics, depth, and object relationships. Google DeepMind's Vision Banana proves that a lightweight instruction-tuning pass over an image generator can beat SAM 3 on segmentation and Depth Anything V3 on metric depth, without a single architectural change.

43 min read

Jun 02, 2026

Cache-Augmented Generation: When Preloaded KV-Caches Replace Retrieval Pipelines

Retrieval-augmented generation fetches documents at query time, scores them, and hopes the retriever got it right. Cache-Augmented Generation sidesteps the entire pipeline by preloading knowledge into the model's KV-cache before a single question is asked, trading storage for speed and eliminating retrieval errors altogether.

22 min read

Jun 02, 2026

Structured Generation and Constrained Decoding: Making LLMs Predictable

Language models generate text one token at a time by sampling from a probability distribution over their entire vocabulary. Constrained decoding intervenes at that sampling step, masking out every token that would violate a target schema, so the model literally cannot produce invalid output. This article traces the theory, engineering, and production reality of making LLMs structurally predictable.

25 min read

Jun 02, 2026

Mixture of Experts: How Frontier LLMs Achieve Scale Without Proportional Cost

Every frontier LLM released since 2023 uses some form of Mixture of Experts. The idea is deceptively simple: build a model with hundreds of billions of parameters but activate only a fraction for each token. The result is dense-model quality at sparse-model cost. Here is how it actually works, where it breaks, and why it dominates.

27 min read