AI Inference Cost Optimization: GPU FinOps in 2026
An AI inference cost optimization decision record: continuous batching, KV-cache, quantization, speculative decoding, spot GPUs, and autoscaling the inference path.
LLM Semantic Router: An Inference Routing Pattern
The LLM semantic router pattern in 2026: route requests by intent and cost to the right model, with vLLM Semantic Router, embeddings, and a reference design.
vLLM Cost Economics: 2026 Deep Dive on $/Million Tokens
A practical 2026 deep dive on vLLM cost economics — KV cache, paged attention, speculative decoding, and dollar-per-million-tokens math.
SGLang vs vLLM vs TensorRT-LLM: 2026 Inference Benchmark
Reproducible 2026 benchmark of SGLang, vLLM, and TensorRT-LLM — throughput, p50/p99, KV cache utilization, and when each wins.
KV Cache Optimization for LLM Inference: A Deep Dive
KV cache optimization for LLM inference — PagedAttention, quantization, prefix caching, and eviction, with the memory math behind each technique.
Q2 2026 LLM Inference Benchmark: vLLM vs TGI vs SGLang vs Triton
Q2 2026 LLM inference benchmark across vLLM, TGI, SGLang, and Triton — throughput, p50/p99 TTFT/TPOT, KV-cache efficiency, and which engine wins per workload class.