Chapters
- 01 Serverless vs Container Cost Efficiency for ML InferenceServerless or containers for ML inference? Compares cold starts, GPU utilization, hidden costs and latency, with a hybrid approach and decision guide.
- 02 DeepSeek V4 Free Trial API: The Complete Practitioner's GuideHow to get started with the DeepSeek V4 free trial API: signup, keys, first call, rate limits, what works and fails on the free tier, and alternatives.
- 03 Run LLM Locally Debian Command Line: The Complete 2026 GuideHow to run an LLM from the command line on Debian: llama.cpp, vLLM and TensorRT-LLM compared, plus quantization, GPU versus CPU, context length and setup.
- 04 DeepSeek V4 API Key: How to Get One and Actually Use ItHow to get a DeepSeek V4 API key and use it: account setup, funding, chat versus reasoner models, third-party libraries, key security, pricing.
- 05 Grok vs GPT vs Claude: App Building Comparison for 2026Grok, GPT and Claude compared for building apps: reasoning, context windows, code generation, multi-agent work, refusals, and how to pick or combine them.
- 06 Circuit Breaker for LLM Inference ServerHow to build a circuit breaker for an LLM inference server: why error rates mislead on GPUs, what to watch, admission control, retry amplification.
- 07 DeepSeek vs GPT-4 Cost Comparison: The Real Math in 2026DeepSeek versus GPT-4 cost beyond price per token: hidden multipliers, latency, privacy, where DeepSeek suits batch and extraction, and where GPT-4 leads.
- 08 CPU vs GPU Inference Cost Efficiency: The 2026 Buying GuideShould inference run on CPU or GPU? A cost-per-token framework covering latency needs, model size, cold starts, engineering time and when an API wins.
- 09 Speculative Decoding: What Is the Acceptance Rate and Why It Matters (2026)What the acceptance rate means in speculative decoding, how it sets the speedup, how to measure it, how to tune the draft model, and common mistakes.
- 10 Why Is LLM Inference Slow? A Practitioner’s Guide to What’s Actually Going OnWhy LLM inference is slow: memory bandwidth limits, attention, serving architecture, quantization, GPU use and batch size, with the order to fix them in.
- 11 Cheapest AI Model for Coding: GPT-4 vs DeepSeek (2026 Guide)Compares DeepSeek and GPT-4 pricing for coding work, with per-token cost examples, hidden costs of cheap models, a hybrid setup and when to pay more.
- 12 Is DeepSeek Better Than ChatGPT?DeepSeek versus ChatGPT on reasoning, math, context window, pricing, creative writing, safety and tooling, and how to choose.
- 13 A Cost-Efficient Model Serving ArchitectureCost-efficient model serving: match compute to demand with hardware choice, quantization, batching, speculative decoding, autoscaling, routing and caching.
- 14 vllm vs llama.cpp debian performance: 2026 Field GuidevLLM versus llama.cpp on Debian: architecture, throughput, setup, serving over an API, quantization trade-offs, cost, and when each is the wrong choice.
- 15 The Best GPU Scheduling Policy for Inference Clusters in 2026How to schedule GPUs for inference clusters: priority queues, shortest-queue routing, admission control, bin-packing, fairness and preemption trade-offs.
- 16 Is DeepSeek Better Than GPT? A Practitioner's Guide to Choosing Your LLMIs DeepSeek better than GPT? Compares coding, math and instruction following, pricing, open-weight benefits and weaknesses, with a model-choice matrix.
- 17 Admission Control vs Autoscaling LLM Inference: The 2026 Buying GuideAdmission control and autoscaling solve different LLM inference problems. See why slow GPU startup makes autoscaling too late and how to combine both.
- 18 Mixture of Experts vs Dense Model Cost: The 2026 Buyer's GuideMixture of experts or dense model? Compares memory, inference compute, latency, training cost and quality, and how to build a cost model for your workload.
- 19 FP8 vs FP16 Inference Cost Efficiency: A Practitioner's Buying GuideFP8 versus FP16 for LLM inference: what each saves, when FP8 is a bad idea, quantization tooling, hardware support, and a framework to choose between them.
- 20 What Is a Disaggregated Inference? The Architecture That Unlocks AI at ScaleWhat disaggregated inference is: splitting prefill, decode and KV cache into separate services. Covers transfer and scheduling costs, and when to use it.
- 21 Serverless vs Containers for AI API Cost 2026: The Honest Buying GuideServerless or containers for an AI API? Compares billing models, cold start costs, memory allocation and GPU platforms, with a hybrid option.