#Roofline

Articles about Roofline — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with roofline. ← All posts

#A/B Testing (3)#A2A (14)#ADK (8)#AG-UI (6)#AI (9)#AI Agents (335)#AI Architecture (22)#AI Cost (10)#AI Cost Optimization (8)#AI Engineering (282)#AI Evaluation (9)#AI Financial Engineering (14)#AI Gateway (8)#AI Governance (40)#AI Red Teaming (9)#AI Research (9)#AI Safety (12)#AI Security (29)#AI in Production (12)#AML (3)#API Design (10)#API Security (8)#APIs (59)#AWS (17)#Accountability (8)#Accounting (9)#Agent Skills (11)#Agentic AI (25)#Agentic Commerce (12)#Agentic RAG (8)#Agents (4)#Amazon Bedrock (8)#Analytics (3)#Architecture (42)#Attention (3)#Audit (4)#Authentication (11)#Authorization (3)#Automation (11)#Azure (11)#Azure AI Foundry (9)#Backend Engineering (310)#Benchmarks (3)#Best Practices (4)#BigQuery (6)#Business Finance (8)#Business Strategy (55)#C (8)#CI/CD (16)#Caching (11)#Capital Markets (14)#Card Payments (12)#Cards (13)#Career (25)#Causal Inference (8)#Cellular Automata (8)#Checkpointing (4)#Claude Code (8)#Cloud (5)#Cloud Architecture (3)#Cloud Native (10)#Code Review (9)#Coding Agents (8)#Collaboration (5)#Communication (9)#Complexity (8)#Compliance (54)#Computer Networking (9)#Computer Science (40)#Computer Vision (15)#Computer-Use Agents (8)#Concurrency (40)#Consulting (5)#Containers (10)#Context Engineering (14)#Conversational AI (8)#Cost Optimisation (5)#Cost Optimization (3)#Credit (16)#Credit Risk (16)#CrewAI (8)#Crypto (12)#Cryptocurrency (12)#Cryptography (9)#Custody (9)#DSPy (8)#Data (14)#Data Engineering (24)#Data Science (8)#Data Structures (10)#Databases (41)#Deep Learning (20)#Deployment (5)#Design (3)#Design Patterns (10)#DevOps (24)#DevSecOps (22)#Developer Experience (5)#Developer Tools (5)#Differential Privacy (3)#Distributed Systems (100)#Documentation (3)#Edge AI (8)#Embeddings (21)#Emotional Intelligence (8)#Energy (8)#Engineering (14)#Engineering Culture (3)#Engineering Judgment (7)#Engineering Practices (16)#Error Handling (4)#Evaluation (68)#Event-Driven Architecture (8)#Explainable AI (8)#FREE-AI (8)#FX (5)#Feature Engineering (3)#Feedback (4)#FinOps (23)#FinTech (6)#Financial AI (14)#Financial Engineering (8)#Financial Systems (129)#Fine-Tuning (12)#Fintech (131)#Flutter (8)#Forecasting (10)#Foreign Exchange (5)#Forward Deployed Engineer (22)#Forward Deployed Engineering (8)#Fraud (10)#Function Tools (5)#Functional Programming (3)#Fundamentals (23)#Fundraising (8)#GCP (5)#GPU (8)#GUI Automation (8)#Gemma (4)#Generative AI (3)#Git (8)#Go (220)#Go-to-Market (13)#Google ADK (36)#Governance (61)#Granite (6)#Graph Neural Networks (8)#GraphQL (3)#Growth (3)#Guardrails (34)#HIPAA (3)#HTTP (3)#Harness Engineering (8)#Hiring (8)#Hugging Face (8)#Human-in-the-Loop (9)#IBM watsonx (8)#Identity (11)#Inference (8)#Information Retrieval (8)#Integration (3)#Intellectual Property (8)#Interfaces (3)#Interpretability (8)#JavaScript (8)#KYC (11)#KYC and AML (12)#Kafka (11)#Knowledge Graphs (10)#Kubernetes (17)#LLM (5)#LLM Inference (8)#LLM Infrastructure (8)#LLM-as-Judge (3)#LLMOps (8)#LLMs (192)#LangChain (8)#LangGraph (11)#Leadership (29)#Ledger (12)#Legal (8)#Lending (14)#Linux (9)#LlamaIndex (8)#Load Balancing (3)#MCP (22)#MLOps (36)#Machine Learning (109)#Marketing (16)#Markets (4)#Mathematical Finance (8)#Memory (15)#Memory Management (5)#Metrics (7)#Microservices (3)#Microsoft Agent Framework (150)#Middleware (6)#Migration (9)#Mixture of Experts (5)#Model Risk (3)#Monitoring (5)#Multi-Agent (10)#Multi-Agent AI (14)#Multi-Agent Systems (73)#Multimodal (3)#Multimodal AI (11)#NIM (5)#NVIDIA (8)#Networking (3)#OAuth (3)#OWASP (7)#Observability (50)#On-Device AI (8)#Ontology (8)#Open Source (7)#OpenTelemetry (5)#Operating Systems (9)#Operations (13)#Opinion (6)#Orchestration (10)#Organizational Design (8)#Payment Rails (16)#Payments (54)#People (8)#Performance (56)#Personalization (9)#Platform Engineering (9)#Policy as Code (3)#PreSales (8)#Privacy (13)#Privacy Engineering (3)#Process (4)#Product (30)#Product Management (8)#Production (22)#Programming (10)#Programming Languages (48)#Prompt Engineering (80)#Prompt Injection (15)#Protocol Buffers (3)#Protocols (9)#Providers (4)#Pydantic AI (8)#Python (142)#Quality (3)#Quantitative Finance (22)#RAG (70)#RBI (3)#REST (5)#Rails (16)#Real-Time (8)#Reasoning Models (8)#Recommender Systems (8)#Reconciliation (3)#RegTech (8)#Regulation (10)#Reinforcement Learning (9)#Reliability (56)#Resilience (4)#Responsible AI (5)#Retrieval (3)#Risk (14)#Risk Management (11)#Roofline (1)#Rust (32)#SLSA (3)#SRE (23)#Sales (9)#Scalability (6)#Search (10)#Security (102)#Security Engineering (8)#Self-Evolving Agents (16)#Semantic Web (8)#Sessions (3)#Settlement (9)#Soft Skills (8)#Software (3)#Software Architecture (36)#Software Delivery (9)#Software Engineering (152)#Spanner (4)#Speech (8)#Startups (30)#Statistics (8)#Strands (8)#Strategy (6)#Stream Processing (8)#Streaming (36)#Structured Output (4)#Supply Chain Security (9)#Sustainability (8)#System Design (32)#Systems Programming (56)#Testing (66)#Threat Modeling (3)#Time Series (9)#Tool Use (23)#Tooling (5)#Tools (4)#Trading (8)#Treasury (6)#Trust (3)#Type System (3)#Type Systems (11)#TypeScript (8)#Validation (3)#Valuation (3)#Vector Databases (22)#Vector Search (11)#Venture Capital (8)#Version Control (9)#Voice AI (9)#Web Development (6)#Workflow (4)#Workflows (14)#eBPF (8)#gRPC (13)#smolagents (8)
Pratik Dhanave · ·5 min read

The Roofline Model — Are You Compute-Bound or Memory-Bound?

Posts 1 and 2 kept drawing a line between two worlds: workloads limited by how fast the GPU can compute, and workloads limited by how fast it can move data. The roofline model is the single tool that tells you which world you're in for any given operation — and therefore which optimizations will help and which are a waste of time. It is the most useful mental model in performance engineering, and it fits on one chart.

The roofline model answers the one question that governs every optimization — compute-bound or memory-bound? — from arithmetic intensity (FLOPs per byte). Why LLM prefill is compute-bound and decode is memory-bound, and why batching is the master throughput lever.

#GPU#Roofline#Performance#Inference#LLMs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.