Inference Systems
Building efficient systems for deploying and running AI models in production with optimal latency and throughput.
Overview
Inference systems focus on efficiently serving AI models in production. Unlike training which happens once, inference happens every time a user interacts with an AI system. Optimizing inference requires different techniques than training: reducing latency for interactive applications, maximizing throughput for batch processing, managing memory constraints, and handling variable workloads. Production inference systems must be reliable, scalable, and cost-effective while maintaining model quality.
Key Research Areas
Low-latency serving for interactive AI
Batch processing for high throughput
Model quantization and compression
Dynamic batching and request scheduling
Multi-model serving infrastructure
Caching and KV-cache optimization
Research Challenges
Balancing latency, throughput, and cost
Memory constraints for large models
Variable request patterns and load
Maintaining quality with quantization
Managing multiple model versions
Cold start and scaling latency
Practical Applications
Serving chatbots and AI assistants
Real-time content recommendation
Search and information retrieval
Code completion and generation
Image and video processing at scale
API services for AI capabilities
Technical Deep Dive
Production inference systems employ various optimization techniques. Quantization reduces model precision from FP16 to INT8 or even lower, dramatically reducing memory and increasing throughput. Continuous batching processes requests as they arrive rather than waiting for full batches. KV-cache stores attention keys and values to avoid recomputation in autoregressive generation. PagedAttention enables efficient memory management for variable-length sequences. Speculative decoding uses smaller draft models to accelerate generation. Model parallelism distributes large models across multiple accelerators. Infrastructure handles load balancing, auto-scaling, and fault tolerance.
Future Research Directions
Future inference systems will handle increasingly large models efficiently. Mixture-of-experts models enable conditional computation for better efficiency. Flash decoding and other algorithmic improvements continue reducing latency. On-device inference for privacy and reduced latency is growing. As models become multimodal, serving systems must efficiently handle diverse input types. Automated optimization and adaptive systems that adjust serving strategies based on workload characteristics will reduce manual tuning.
Related Research Topics
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