500+ LLM Inference Optimization Techniques You Need to Know in 2026

500+ LLM Inference Optimization Techniques in 2026

The landscape of LLM inference has exploded. Here’s what’s new and what matters.

What Changed in 2026

The biggest shift: sparse attention is going mainstream. MiniMax Sparse Attention (MSA) and SubQuadratic Sparse Attention (SSA) are replacing dense attention for long-context scenarios, with Dynamic Hierarchical Sparse Attention (DHSA) offering the best tradeoff.

Key Breakthroughs

Quantization

• FR-Spec — vocab shortlisting optimization
• OSCAR — 2-bit KV compression
• Lossless quantization — sub-4-bit precision

Attention

• KV sharing, KV pinning, KV reversal & correction
• KIVI & SnapKV — adaptive attention windows

Kernel Optimizations

• Flash Attention v4 — polynomial Softmax approximation
• Kernel synthesis — auto-generate per hardware
• Whole layer fused kernels

Prefill & Decode

• Shallow prefill — cheaper initial processing
• Layerwise Pipelined Prefill-Decoding

The Bottom Line

The old “just use quantized GGUF” approach leaves 2-5x performance on the table. Sparse attention + kernel fusion = where the real gains are in 2026.

Source: Aussie AI — 700+ research papers, updated June 2026.