AI Inference Guide
Core Concepts
Deployment Options
Tools & Services
Advanced Topics
Advanced Inference Optimization
Cutting-Edge Optimization Techniques
The latest advances in inference optimization are delivering dramatic performance improvements, with some techniques achieving 2.5x speedup while reducing memory usage by 30% or more.
Speculative Decoding Evolution
Advanced techniques for predicting and pre-computing likely token sequences
Dynamic Speculation Lookahead (DISCO)
Dynamically adjusts speculation length based on context complexity
QuantSpec Self-Speculative Decoding
Uses hierarchical quantized KV cache for efficient speculation
Test-Time Compute Scaling
Allocates more compute during inference for better reasoning
Memory Architecture Advances
Next-generation memory systems for large-scale inference
Big Memory Architectures
Essential for context-aware AI agents with long interaction histories
Hierarchical KV Caching
Multi-tier caching strategies for different attention patterns
Memory-Optimized Architectures
Purpose-built designs for inference workloads
Mixture of Experts (MoE) Advances
Smart routing and expert selection for specialized inference
Symbolic MoE
Skill-based routing for heterogeneous reasoning tasks
Patched MoA
Optimized mixture of agents for software development tasks
Adaptive Expert Selection
Dynamic instance-level mixing of pre-trained experts
Implementation Priority Matrix
Quick Wins (Easy Implementation)
- • KV caching optimization
- • Basic speculative decoding
- • Memory-efficient batching
- • Context compression
Advanced Techniques (Complex Implementation)
- • Dynamic speculation lookahead
- • Hierarchical quantized systems
- • Multi-expert routing
- • Test-time compute scaling
Advanced Inference Optimization
Cutting-Edge Optimization Techniques
The latest advances in inference optimization are delivering dramatic performance improvements, with some techniques achieving 2.5x speedup while reducing memory usage by 30% or more.
Speculative Decoding Evolution
Advanced techniques for predicting and pre-computing likely token sequences
Dynamic Speculation Lookahead (DISCO)
Dynamically adjusts speculation length based on context complexity
QuantSpec Self-Speculative Decoding
Uses hierarchical quantized KV cache for efficient speculation
Test-Time Compute Scaling
Allocates more compute during inference for better reasoning
Memory Architecture Advances
Next-generation memory systems for large-scale inference
Big Memory Architectures
Essential for context-aware AI agents with long interaction histories
Hierarchical KV Caching
Multi-tier caching strategies for different attention patterns
Memory-Optimized Architectures
Purpose-built designs for inference workloads
Mixture of Experts (MoE) Advances
Smart routing and expert selection for specialized inference
Symbolic MoE
Skill-based routing for heterogeneous reasoning tasks
Patched MoA
Optimized mixture of agents for software development tasks
Adaptive Expert Selection
Dynamic instance-level mixing of pre-trained experts
Implementation Priority Matrix
Quick Wins (Easy Implementation)
- • KV caching optimization
- • Basic speculative decoding
- • Memory-efficient batching
- • Context compression
Advanced Techniques (Complex Implementation)
- • Dynamic speculation lookahead
- • Hierarchical quantized systems
- • Multi-expert routing
- • Test-time compute scaling