Constrained Decoding: Architecture for Guaranteed-Valid LLM Output (2026)
How constrained decoding guarantees valid LLM output: grammars, FSAs, token masking, JSON-schema enforcement, and where structured generation breaks in production.
On-Device SLM Inference: A 2026 Edge GPU Benchmark
A 2026 benchmark methodology for small language models on edge GPUs — latency, tokens/sec, memory, and cost for Phi, Gemma, and Qwen on Jetson-class hardware.
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.
OpenAI o3 Reasoning Models: Test-Time Compute Scaling Explained
How OpenAI's o3 family scales reasoning at inference time — chain-of-thought RL, verifier models, cost curves, and when test-time compute beats pre-training.