LLM Production Ops
Practical guides for engineers shipping LLMs to production — inference engines, cost optimization, observability, and deployment. No fluff, just what works.
LLM Inference Engines Hub
Production guide to vLLM, SGLang, TGI, and TensorRT-LLM. Start here for engine selection and deployment.
Start herevLLM vs SGLang vs TGI: 2026 Benchmark
Head-to-head comparison on H100 — throughput, latency, VRAM, and when to pick each engine for production serving.
ComparisonHow to Deploy vLLM in Production
Docker setup, tensor parallelism, GPU memory tuning, and the OpenAI-compatible API.
TutorialSGLang RadixAttention Explained
The radix-tree prefix cache behind SGLang's 29% throughput edge on agents and RAG.
Deep DivevLLM PagedAttention
The paged KV cache that eliminated fragmentation and hit 96% GPU utilization — the foundation of high-throughput serving.
Deep DiveTensorRT-LLM Guide
NVIDIA's optimized inference engine — when its lower latency beats vLLM, and when the build-step cost isn't worth it.
Deep DiveGPU Benchmark: H100 vs A100 vs L40
Cost-per-token across GPU generations. Why memory bandwidth — not TFLOPs — decides which card wins for your workload.
ComparisonLLM Gateways & Cost Hub
Route requests across providers, cache redundant calls, and cut API spend 70-85%. Start here for gateway selection.
Start hereLiteLLM vs Portkey vs OpenRouter
Three-way comparison — routing, fallbacks, cost control, observability, self-hosting, and pricing for 2026.
ComparisonLLM Cost Optimization Playbook
The 5 levers that cut API spend 70-85%: routing, semantic caching, context compaction, compression, budget governance.
PlaybookHow to Self-Host LiteLLM
Production Docker Compose setup with PostgreSQL, team keys, budgets, cost tracking, and rate limits.
TutorialSemantic Caching for LLM APIs
How vector-similarity caching intercepts 25-40% of redundant calls — GPTCache, Redis VL, and the threshold that matters.
Deep DiveLLM Model Routing
Route requests by complexity — rule-based, classifier-based, and cascade patterns to send each query to the cheapest capable model.
Deep DiveLLM Pricing Comparison
OpenAI vs Anthropic vs Gemini — per-token pricing by tier (frontier, budget, cache-discount, batch) for 2026.
ComparisonLLM Observability & Evals Hub
Trace every call, evaluate quality at scale, catch hallucinations. LangSmith, Langfuse, Phoenix — start here.
Start hereLangSmith vs Langfuse vs Phoenix
Tracing, evals, pricing, open-source status — which LLM observability platform to pick for 2026.
ComparisonHow to Self-Host Langfuse
Production Docker Compose setup for open-source LLM observability — tracing, evaluation, dashboards.
TutorialLLM-as-a-Judge Methodology
Using a strong model to score outputs — biases, human correlation, and trustworthy scoring practices.
Deep DiveAgent Tracing
Multi-span traces for tool-using agents — where latency hides, and the span hierarchy that surfaces it.
Deep DiveLLM Cost Monitoring
Per-team, per-model spend dashboards. The cost formula and the three dashboards that actually matter.
Deep DiveHallucination Detection in Production
RAG grounding checks, self-consistency, LLM-as-judge, and guardrails — the layered defense against fabrications.
Deep DiveLLM Inference Optimization Hub
The techniques that decide 20 vs 200 tokens/sec: Flash Attention, KV cache, prefix caching, speculative decoding.
Start hereFlash Attention Explained
The tiled attention kernel that cut memory O(N²)→O(N). FA1, FA2 (2x), FA3 (1.5-2x on H100, 740 TFLOPs/s).
Deep DivePrefix Caching in vLLM & SGLang
Block-level hashing (vLLM) vs radix tree (SGLang) — how prefix caching skips redundant prefill.
Deep DiveChunked Prefill
Break prefill into chunks so decode never stalls — cuts TTFT 2-4x and lifts throughput 1.5-2x on mixed traffic.
Deep DiveSpeculative Decoding
Draft-verify decoding for 2-3x decode throughput with zero quality loss. Math, variants (Medusa, EAGLE), deployment.
Deep DiveMulti-Head Latent Attention (MLA)
DeepSeek's KV cache compression — cuts memory 93% vs standard MHA while keeping quality.
Deep DiveRing Attention
Scaling context to millions of tokens across GPUs. Sequence parallelism, the ring topology, when it beats naive sharding.
Deep DiveKV Cache Quantization (FP8/INT4)
How FP8 and INT4 KV cache quantization slash memory 2-8x with <1% quality drop. Implementation in vLLM and SGLang.
Deep DiveWhat is RAG? Retrieval-Augmented Generation
How RAG grounds LLM responses in retrieved documents, cutting hallucinations 40-70%. The 2026 production stack, vector DB choice, and when RAG beats fine-tuning.
ConceptLoRA Fine-Tuning in 2026
LoRA freezes base weights and trains tiny low-rank matrices — 10-100x less memory than full fine-tuning. Rank choice, QLoRA for 70B models, multi-LoRA serving.
Concept