From Tokenization to Output

Posted on by lechuck park

From Tokenization to Output: Understanding NLP and Transformer Models

This image illustrates the complete process from tokenization to output in Natural Language Processing (NLP) and transformer models.

Top Section: Traditional Information Retrieval Process (Green Boxes)

  1. Distinction (Difference) – Clear Boundary
    • Cutting word pieces, attaching number tags, creating manageable units, generating receipt slips
  2. Classification (Similarity)
    • Placing in the same neighborhood, gathering similar meanings, classifying by topic on bookshelves, organizing by close proximity
  3. Indexing
    • Remembering position, assigning bookshelf numbers, creating a table of contents, organizing context
  4. Retrieval (Fetching)
    • Asking a question, searching the table of contents, retrieving content, finding necessary information
  5. Processing → Result
    • Analyzing information, synthesizing content, writing a report, generating the final answer

Bottom Section: Actual Transformer Model Implementation (Purple Boxes)

  1. Tokenization
    • String splitting, subword units, ID conversion, vocabulary mapping
  2. Embedding Feature
    • High-dimensional vector conversion, embedding matrix, semantic distance, placement in vector space
  3. Positional Encoding + Context Building
    • Positional information encoding, sine/cosine functions, context matrix, preserving sequence order
  4. Attention Mechanism
    • Query-Key-Value, attention scores, softmax weights, selective information extraction
  5. Feed Forward + Output
    • Non-linear transformation, 2-layer neural network, softmax probability distribution, next token prediction

Key Concept

This diagram maps traditional information retrieval concepts to modern transformer architecture implementations. It visualizes how abstract concepts in the top row are realized through concrete technical implementations in the bottom row, providing an educational resource for understanding how models like GPT and BERT work internally at each stage.

Summary

This diagram explains the end-to-end pipeline of transformer models by mapping traditional information retrieval concepts (distinction, classification, indexing, retrieval, processing) to technical implementations (tokenization, embedding, positional encoding, attention mechanism, feed-forward output). The top row shows abstract conceptual stages while the bottom row reveals the actual neural network components used in models like GPT and BERT. It serves as an educational bridge between high-level understanding and low-level technical architecture.

#NLP #TransformerModels #DeepLearning #Tokenization #AttentionMechanism #MachineLearning #AI #NeuralNetworks #GPT #BERT #PositionalEncoding #Embedding #InformationRetrieval #ArtificialIntelligence #DataScience

With Claude

Multi-Head Latent Attention – Changes

Posted on by lechuck park

Multi-Head Latent Attention (MLA) Interpretation

This image is a technical diagram explaining the structure of Multi-Head Latent Attention (MLA).

🎯 Core Concept

MLA is a mechanism that improves the memory efficiency of traditional Multi-Head Attention.

Traditional Approach (Before) vs MLA

Traditional Approach:

  • Stores K, V vectors of all past tokens
  • Memory usage increases linearly with sequence length

MLA:

  • Summarizes past information with a fixed-size Latent vector (c^KV)
  • Maintains constant memory usage regardless of sequence length

📊 Architecture Explanation

1. Input Processing

  • Starts from Input Hidden State (h_t)

2. Latent Vector Generation

  • Latent c_t^Q: For Query of current token (compressed representation)
  • Latent c_t^KV: For Key-Value (cached and reused)

3. Query, Key, Value Generation

  • Query (q): Generated from current token (h_t)
  • Key-Value: Generated from Latent c_t^KV
    • Creates Compressed (C) and Recent (R) versions from c_t^KV
    • Concatenates both for use

4. Multi-Head Attention Execution

  • Performs attention computation with generated Q, K, V
  • Uses BF16 (Mixed Precision)

✅ Key Advantages

  1. Memory Efficiency: Compresses past information into fixed-size vectors
  2. Faster Inference: Reuses cached Latent vectors
  3. Information Preservation: Maintains performance by combining compressed and recent information
  4. Mixed Precision Support: Utilizes FP8, FP32, BF16

🔑 Key Differences

  • v_t^R from Latent c_t^KV is not used (purple box on the right side of diagram)
  • Value of current token is directly generated from h_t
  • This enables efficient combination of compressed past information and current information

This architecture is an innovative approach to solve the KV cache memory problem during LLM inference.

Summary

MLA replaces the linearly growing KV cache with fixed-size latent vectors, dramatically reducing memory consumption during inference. It combines compressed past information with current token data through an efficient attention mechanism. This innovation enables faster and more memory-efficient LLM inference while maintaining model performance.

#MultiHeadLatentAttention #MLA #TransformerOptimization #LLMInference #KVCache #MemoryEfficiency #AttentionMechanism #DeepLearning #NeuralNetworks #AIArchitecture #ModelCompression #EfficientAI #MachineLearning #NLP #LargeLanguageModels

With Claude

Human with AI

Posted on by lechuck park

This image titled “Human with AI” illustrates the collaborative structure between humans and AI.

Top: Human works

Humans operate through three stages:

  1. Experience – Collecting various experiences and information
  2. Thought – Thinking and judging by combining emotions, logic, and intuition
  3. Action – Executing final decisions

Bottom: AI Works

AI operates through similar three stages:

  1. Learning – Learning from databases and patterns
  2. Reasoning – Analyzing and judging through algorithms and calculations
  3. Inference – Deriving results based on statistics and probabilities

Core: Human-AI Collaboration Structure

The green arrow in the center with “Develop & Verification” represents the process where humans verify AI’s reasoning results and make final judgments (Thought) to connect them to actual actions (Action).

In other words, when AI analyzes data and presents reasoning results, humans review and verify them to ultimately decide whether to execute – representing a Human-in-the-loop system. AI assists decision-making, but the final judgment and action are under human responsibility.

Summary

This diagram illustrates a Human-in-the-loop AI system where AI processes data and provides reasoning, but humans retain final decision-making authority. Both humans and AI follow similar learning-thinking-acting cycles, but human verification serves as the critical bridge between AI inference and real-world action. This structure emphasizes responsible AI deployment with human oversight.

#HumanAI #AICollaboration #HumanInTheLoop #AIGovernance #ResponsibleAI #AIDecisionMaking #HumanOversight #AIVerification #HumanCenteredAI #AIEthics

With Claude

Who is the first wall?

Posted on by lechuck park

AI Scaling: The 6 Major Bottlenecks (2025)

1. Data

  • High-quality text data expected to be depleted by 2026
  • Solutions: Synthetic data (fraud detection in finance, medical data), Few-shot learning

2. LLM S/W (Algorithms)

  • Ilya Sutskever: “The era of simple scaling is over. Now it’s about scaling the right things”
  • Innovation directions: Test-time compute scaling (OpenAI o1), Mixture-of-Experts architecture, Hybrid AI

3. Computing → Heat

  • GPT-3 training required 1,024 A100 GPUs for several months
  • By 2030, largest training runs projected at 2-45GW scale
  • GPU cluster heat generation makes cooling a critical challenge

4. Memory & Network ⚠️ Current Critical Bottleneck

Memory

  • LLMs grow 410x/2yr, computing power 750x/2yr vs DRAM bandwidth only 2x/2yr
  • HBM3E completely sold out for 2024-2025. AI memory market projected to grow at 27.5% CAGR

Network

  • Speed of light limitation causes tens to hundreds of ms latency over distance. Critical for real-time applications (autonomous vehicles, AR)
  • Large-scale GPU clusters require 800Gbps+, microsecond-level ultra-low latency

5. Power 💡 Long-term Core Constraint

  • Sam Altman: “The cost of AI will converge to the cost of energy. The abundance of AI will be limited by the abundance of energy”
  • Power infrastructure (transmission lines, transformers) takes years to build
  • Data centers projected to consume 7.5% of US electricity by 2030

6. Cooling

  • Advanced technologies like liquid cooling required. Infrastructure upgrades take 1+ year

“Who is the first wall?”

Critical Bottlenecks by Timeline:

  1. Current (2025): Memory bandwidth + Data quality
  2. Short-to-Mid term: Power infrastructure (5-10 years to build)
  3. Long-term: Physical limit of the speed of light

Summary

The “first wall” in AI scaling is not a single barrier but a multi-layered constraint system that emerges sequentially over time. Today’s immediate challenges are memory bandwidth and data quality, followed by power infrastructure limitations in the mid-term, and ultimately the fundamental physical constraint of the speed of light. As Sam Altman emphasized, AI’s future abundance will be fundamentally limited by energy abundance, with all bottlenecks interconnected through the computing→heat→cooling→power chain.

#AIScaling #AIBottleneck #MemoryBandwidth #HBM #DataCenterPower #AIInfrastructure #SpeedOfLight #SyntheticData #EnergyConstraint #AIFuture #ComputingLimits #GPUCluster #TestTimeCompute #MixtureOfExperts #SamAltman #AIResearch #MachineLearning #DeepLearning #AIHardware #TechInfrastructure

With Claude

Data Center Shift with AI

Posted on by lechuck park

Data Center Shift with AI

This diagram illustrates how data centers are transforming as they enter the AI era.

📅 Timeline of Technological Evolution

The top section shows major technology revolutions and their timelines:

  • Internet ’95 (Internet era)
  • Mobile ’07 (Mobile era)
  • Cloud ’10 (Cloud era)
  • Blockchain
  • AI(LLM) ’22 (Large Language Model-based AI era)

🏢 Traditional Data Center Components

Conventional data centers consisted of the following core components:

  • Software
  • Server
  • Network
  • Power
  • Cooling

These were designed as relatively independent layers.

🚀 New Requirements in the AI Era

With the introduction of AI (especially LLMs), data centers require specialized infrastructure:

  1. LLM Model – Operating large language models
  2. GPU – High-performance graphics processing units (essential for AI computations)
  3. High B/W – High-bandwidth networks (for processing large volumes of data)
  4. SMR/HVDC – Switched-Mode Rectifier/High-Voltage Direct Current power systems
  5. Liquid/CDU – Liquid cooling/Cooling Distribution Units (for cooling high-heat GPUs)

🔗 Key Characteristic of AI Data Centers: Integrated Design

The circular connection in the center of the diagram represents the most critical feature of AI data centers:

Tight Interdependency between SW/Computing/Network ↔ Power/Cooling

Unlike traditional data centers, in AI data centers:

  • GPU-based computing consumes enormous power and generates significant heat
  • High B/W networks consume additional power during massive data transfers between GPUs
  • Power systems (SMR/HVDC) must stably supply high power density
  • Liquid cooling (Liquid/CDU) must handle high-density GPU heat in real-time

These elements must be closely integrated in design, and optimizing just one element cannot guarantee overall system performance.

💡 Key Message

AI workloads require moving beyond the traditional layer-by-layer independent design approach of conventional data centers, demanding that computing-network-power-cooling be designed as one integrated system. This demonstrates that a holistic approach is essential when building AI data centers.

📝 Summary

AI data centers fundamentally differ from traditional data centers through the tight integration of computing, networking, power, and cooling systems. GPU-based AI workloads create unprecedented power density and heat generation, requiring liquid cooling and HVDC power systems. Success in AI infrastructure demands holistic design where all components are co-optimized rather than independently engineered.

#AIDataCenter #DataCenterEvolution #GPUInfrastructure #LiquidCooling #AIComputing #LLM #DataCenterDesign #HighPerformanceComputing #AIInfrastructure #HVDC #HolisticDesign #CloudComputing #DataCenterCooling #AIWorkloads #FutureOfDataCenters

With Claude