Batch Inference Patterns for AI Workloads
Processing large volumes of AI inference requests efficiently. Queue design, throughput optimization, error handling, and cost management …
Inference
Batch vs Real-Time Inference Patterns
Comparing batch and real-time inference patterns for ML models, covering architecture, cost, latency, and when to use each approach.
Building gRPC Microservices for ML Inference
How to build gRPC-based microservices for ML inference: proto definitions, streaming token delivery, load balancing, health checks, and …
Capacity Planning for AI Inference
How to right-size GPU and TPU clusters, configure autoscaling for inference workloads, manage GPU memory, and plan capacity for variable AI …
Direct Model Interface - The Simplest AI Integration Pattern
The foundational pattern: user input goes to a model API, model response comes back. When this is enough and when you need something more.
Edge Computing
What edge computing is, how it brings computation closer to data sources, and when edge deployment is appropriate for AI workloads.
Flash Attention
How Flash Attention makes transformer self-attention memory-efficient by restructuring computation to minimize GPU memory reads and writes.
GPU vs TPU for AI Training and Inference
Comparing GPUs and TPUs for AI model training and inference, covering performance, cost, ecosystem, and workload suitability.
Inference-Time Compute
The practice of allocating additional computation during model inference to improve reasoning quality, including chain-of-thought, search, …
Model Tier Routing - Matching Request Complexity to Model Cost
Route AI requests to different model tiers based on complexity, cost sensitivity, and quality requirements. Reduce spend without sacrificing …
Performance Engineering for AI Systems
A comprehensive guide to latency optimization, GPU memory management, throughput engineering, and model acceleration techniques for …
Real-Time Feature Computation Pattern
The architectural pattern for computing ML features from event streams: windowed aggregations, stream-table joins, dual-write to online and …
Reducing LLM Inference Costs in Production
Practical strategies for reducing LLM API and hosting costs without sacrificing quality, from caching and routing to model selection and …
Token Budget
The maximum number of tokens allocated for an LLM request or workflow, used to control costs, latency, and context window utilization.
vLLM - High-Performance LLM Serving Engine
vLLM is an open-source library for high-throughput, low-latency serving of large language models using PagedAttention memory management.
Inference - Running AI Models in Production
What inference means in AI context, the key operational parameters that matter (latency, throughput, cost), and the main deployment options …
Why Your AI Output Sounds Generic - And How to Fix It With Your Own Data
The difference between prompting and grounding. Five stages from zero context to production-ready assets. The Personal Inference Pack …