chrislabs.ai

The world of AI & infrastructure, explained for builders and adopters.

Fundamental but genuinely technical knowledge for the next generation of sales, sales engineers, and ML, data, and platform engineers - from tokens and attention to GPUs, serving, training, and the VAST Data platform.

Start with the foundationsExplore the VAST track

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Every page has Prev / Next links to follow the full order.

A short path across the three core pillars - how models read text, why inference is memory-bound, and the hardware that runs it.

  1. Step 1Tokenization & TransformersHow text becomes tokens and how self-attention relates them.
  2. Step 2KV CacheThe memory vault that makes token generation fast.
  3. Step 3NVIDIA InfrastructureGPU models, specs, workloads, interconnect, and offerings.

Foundations

How language models read, represent, and generate text.

Tokenization & Transformers

Fundamental

How text becomes tokens and how self-attention relates them.

Attention Mechanisms

Advanced

MHA, MQA, GQA, MLA, sliding-window - and their memory cost.

Model Architectures

Intermediate

Dense, sparse MoE, transformers, Mamba/SSMs, and multimodal.

Memory & Efficiency

Why inference is memory-bound, and how to tame it.

KV Cache

Fundamental

The memory vault that makes token generation fast.

Model Sizing & Parallelism

Intermediate

Parameters, memory math, and tensor/pipeline/data parallelism.

Quantization & Precision

Intermediate

FP16/BF16/FP8/INT4 and the memory-vs-quality tradeoff.

Systems & Infrastructure

The hardware and software stack that runs AI at scale.

NVIDIA Infrastructure

Fundamental

GPU models, specs, workloads, interconnect, and offerings.

NVIDIA STX & Context Memory

Advanced

BlueField-4 STX, the CMX KV-cache tier, and VAST's role.

Inference & Serving

Intermediate

Prefill vs decode, batching, and disaggregated serving.

Training & Fine-Tuning

Intermediate

Pretraining, LoRA/QLoRA, checkpoints, and resourcing.

Simulation & Digital Twins

Advanced

Omniverse, world models, and simulation as a GPU workload.

Data Pipeline for AI

Fundamental

Where storage and the lakehouse sit in the AI lifecycle.

Applications

Patterns that turn models into products.

RAG & Vector Search

Intermediate

Embeddings, retrieval, and grounding models in your data.

Agentic AI

Advanced

Agents, context, memory, and tool use at scale.

VAST Foundation Stacks

Advanced

Production RAG, AI-Q research, and video search on NVIDIA Blueprints.

VAST Data

The data platform for the AI era.

VAST Overview

Fundamental

Why VAST matters for AI, data lakes, and agents.

VAST Architecture

Intermediate

DASE: disaggregated, shared-everything architecture.

VAST DataSpace

Intermediate

One global namespace across edge, on-prem, and every cloud.

VAST DataBase

Intermediate

A columnar table format - an Iceberg/Delta alternative, native to the platform.

Secure Multitenancy

Intermediate

Tenant isolation, access control, and QoS on one shared platform.

VAST VectorStore

Advanced

Vector search at scale, native to the data platform.

DataEngine & Event Broker

Intermediate

Serverless functions, triggers, and Kafka-compatible streaming.

AgentEngine & MCP

Advanced

Running and governing AI agents next to the data.

InsightEngine (NVIDIA)

Intermediate

Real-time RAG and the NVIDIA AI data-platform stack.

Roadmap & Vision

Fundamental

The AI OS: Polaris, GPU SQL, and the agentic future.

Glossary

Fundamental

Plain-language definitions of the key terms.