How LLMs Like ChatGPT Actually Work: System Design Perspective

The LLM Revolution

Large Language Models like ChatGPT have transformed how we interact with computers. But what does it take to build and deploy systems at this scale? Let's explore from a system design perspective.

Training Infrastructure

The Scale:

• GPT-4 estimated at 1.7 trillion parameters
• Training on trillions of tokens of text
• Thousands of GPUs for months
• Cost: $100M+ per training run

Training Architecture:

1. Data Pipeline

• Petabytes of training data
• Cleaning, deduplication, filtering
• Tokenization and preprocessing
• Distributed storage (often custom)

2. Compute Cluster

• Thousands of GPUs/TPUs
• High-bandwidth interconnects (NVLink, InfiniBand)
• Optimized collective communications
• Failure handling and checkpointing

3. Training Framework

• Distributed training (data, model, pipeline parallelism)
• Mixed precision training (FP16/BF16)
• Gradient checkpointing
• Custom kernels for efficiency

Model Parallelism Strategies

Data Parallelism:

• Same model on each GPU
• Different data batches
• Gradients averaged across GPUs
• Scales to moderate sizes

Tensor Parallelism:

• Single layer split across GPUs
• Each GPU holds part of each tensor
• High communication overhead
• For very large layers

Pipeline Parallelism:

• Different layers on different GPUs
• Micro-batches flow through pipeline
• Bubble overhead at pipeline stages
• Balances communication/computation

ZeRO (Zero Redundancy Optimizer):

• Partitions optimizer states, gradients, parameters
• Each GPU holds fraction of each
• Enables training models that don't fit on one GPU

Inference Architecture

Serving Challenges:

• Low latency (sub-second responses)
• High throughput (millions of users)
• Cost efficiency (GPUs are expensive)
• Variable-length requests

Key Optimizations:

1. Batching

• Group multiple requests
• Fill GPU memory efficiently
• Dynamic batching for varied lengths

2. KV Cache

• Cache key-value tensors from previous tokens
• Avoid recomputation
• Memory-bound, not compute-bound

3. Model Optimizations

• Quantization (FP16 → INT8 → INT4)
• Pruning (remove unnecessary weights)
• Distillation (smaller student models)

4. Speculative Decoding

• Small model drafts tokens
• Large model verifies in parallel
• Reduces latency for long outputs

Scaling Inference

Multi-GPU Inference:

• Tensor parallelism for large models
• Model doesn't fit on single GPU
• Lower latency, higher cost per request

Batching Strategies:

Static Batching:

• Wait for N requests
• Process together
• Simple but inefficient for varied lengths

Continuous Batching:

• Add requests as slots free up
• Better utilization
• More complex scheduling

Iteration-level Batching:

• Insert new requests each iteration
• Maximize GPU utilization
• State-of-the-art approach

Cost Optimization

The Problem:

• GPU inference is expensive
• $0.01-0.10 per 1K tokens typical
• At scale, costs are enormous

Solutions:

1. Smaller Models

• Use smallest model that meets quality bar
• GPT-3.5 vs GPT-4 cost difference is 20-30x

2. Caching

• Cache common queries
• Semantic similarity caching
• Significant cost savings for repetitive requests

3. Request Routing

• Simple queries → small models
• Complex queries → large models
• Classification model for routing

4. Self-Hosted Models

• Open-source models (Llama, Mistral)
• Higher upfront cost, lower marginal cost
• Makes sense at scale

Reliability Challenges

Model Behavior:

• Non-deterministic outputs
• Hallucinations
• Prompt injection vulnerabilities
• Content safety concerns

Infrastructure:

• GPU failures
• Memory errors
• Network partitions
• Version management

Mitigations:

• Output validation
• Content filters
• Guardrails and sandboxing
• Graceful degradation

Real-time Features

Streaming Responses:

• Token-by-token delivery
• Reduces perceived latency
• WebSocket or SSE transport
• Partial response handling

Function Calling:

• Structured output generation
• Tool use and agents
• Reliable parsing requirements
• Retry and validation logic

Architecture Patterns

ChatGPT-like System:

1. **API Gateway**: Authentication, rate limiting
2. **Request Queue**: Handle traffic spikes
3. **Inference Service**: Model execution
4. **Context Service**: Conversation history
5. **Safety Service**: Content filtering
6. **Analytics**: Usage tracking

Interview Application

When designing LLM systems:

Key Questions:

• Latency requirements
• Throughput expectations
• Quality requirements
• Cost constraints
• Safety requirements

Discussion Points:

• Model selection trade-offs
• Caching strategies
• Scaling approaches
• Safety and guardrails

Trade-offs:

• Latency vs cost (batching)
• Quality vs cost (model size)
• Flexibility vs safety (guardrails)

Understanding LLM infrastructure is increasingly important as these systems become core to modern applications.