LLM infrastructure,
without the fluff.

A how-to for fitting a 70B-parameter model onto one 24 GB RTX 4090 using aggressive 4-bit and 2-bit quantization — what works, what breaks, and the accuracy cost.

How Anthropic prompt caching works, what it costs to write and read the cache, and the conditions under which it cuts input token spend by up to 90%.

A benchmark-driven comparison of AWQ and GPTQ post-training quantization — accuracy, speed, and memory — so you can pick the right method instead of guessing.

How accuracy degrades and memory shrinks as you drop from 8-bit to 4-bit to 2-bit quantization, and how to find the sweet spot for your model and hardware.

How to partition your semantic cache by query category so similar-but-different intents don't collide, and why heterogeneous workloads break naive semantic caching.

Context engineering — deciding what goes into the model's context window, in what form and order — and why it closes most of the accuracy gap teams reach for fine-tuning to fix.

Four proven techniques to reduce LLM API token spend in production — system prompt optimization, output controls, model routing, and prompt caching — without degrading output quality.

A practical guide to choosing between GGUF, AWQ, and GPTQ quantization formats based on your runtime, hardware, and accuracy needs.

The expensive parts of a production LLM application are rarely the obvious ones. Four hidden cost drivers — and the monitoring setup that catches them before they hit the invoice.

For healthcare, fintech, and European companies, the LLM compliance question isn't primarily about cost — it's about what data can legally leave your infrastructure, and under what conditions.

How to deploy, scale, and manage open-source LLM inference workloads on Kubernetes using llm-d — the operator-based framework built for production GPU clusters.

How to configure and verify KV cache reuse in vLLM to cut repeated-prefix inference costs, with concrete steps and the metrics to watch.

A step-by-step walkthrough of LiteLLM's Router class — defining model pools, configuring multi-provider fallbacks, enabling cost-based routing, and adding task-specific pools for math, code, and creative tasks.

Enterprise LLM costs don't grow linearly with usage — five organizational and architectural patterns compound on each other to multiply spend. Here's what they are and how to fix them.

A beginner's guide to LLM quantization: how INT4, INT8, and FP16 trade memory for quality, and the rule of thumb for sizing a model to your GPU.

Does this request actually need your most expensive model? Semantic routing answers that question automatically — before the expensive model ever sees it.

LoRA vs full fine-tuning: how they differ in GPU cost, trainable parameters, and accuracy — and when each is the right choice.

How to stack semantic, prefix, and inference-layer caches into a single pipeline that maximises hit rate while controlling cost and staleness.

A case study on warming LLM caches with predictable queries overnight so support bots hit cache on the first message of the day instead of paying full inference cost.

A side-by-side comparison of how OpenAI and Anthropic implement prompt caching — automatic vs manual, TTLs, pricing, and which fits which workload.

How PagedAttention borrows OS virtual-memory paging to eliminate KV cache fragmentation, and why it lets vLLM reach up to 14× higher throughput.

Most routing systems decide before the model does any work. Activation routing flips that — it reads what happens inside the model during prefill and uses those signals to decide whether to escalate.

The practical difference between prefix caching (exact-match on token sequences) and semantic caching (embedding similarity), and how to pick the right one for your use case.

The exact formula for calculating your prompt caching break-even point — factoring in write premium, read discount, TTL, and request volume — so you know whether caching is worth it before you turn it on.

A case study on shrinking LLMs to run under 4 GB of VRAM for edge deployment — which quantization methods survive the squeeze and where quality falls apart.

A step-by-step guide to quantizing Llama 3 to 4-bit precision while keeping accuracy loss minimal — calibration data, method choice, and verification.

RouteLLM, semantic-router, and vLLM each solve a different layer of the routing problem. Here's what each tool actually does, where they overlap, and how to choose.

Every team scaling an LLM product eventually runs this comparison. Most get it wrong because they only count compute. Here's the full cost stack — and the exact token volume where the math flips.

Most LLM routers make one decision and commit. Signal-driven MoE routing makes continuous routing decisions across a request's full lifecycle — before generation, during generation, after generation — driven by signals from the query, the output, the system, and history.

Why naive INT8 breaks on large models, and how SmoothQuant and activation-aware methods like AWQ recover near-FP16 accuracy.

A framework decision that's easy to get wrong — they look similar on the surface but are built for fundamentally different use cases. Plus a step-by-step guide to running Llama 4 Scout on a single GPU.

What the research on automatic routing between standard and reasoning models found — which task types justify the cost premium, what the accuracy tradeoff looks like, and how to automate the decision.