Deep dives between the course and the docs: each guide explains one decision you will actually have to make, with the trade-offs spelled out.
How much VRAM does a local LLM need? Learn the weights-plus-KV-cache formula, what 4-bit quantization saves, and which GPU fits Llama 3 8B or 70B.
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Learn how to price an AI feature: estimate LLM cost per request and per user, set usage limits, and protect gross margin with a simple worked example.
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Prompt caching and batch APIs can cut LLM bills sharply. See how cached input and batch discounts work, with a worked cost example and checklist.
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Compare GPT-4o mini, Claude Haiku and Gemini Flash on token pricing, context handling and real workload costs, with a practical calculator walkthrough.
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A practical breakdown of DeepSeek API pricing, cache hits, and output costs, with a calculator walkthrough to see when it is truly cheaper.
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Free LLM APIs come with rate limits, token caps, and hidden catches. Learn what free tiers really offer and how to plan around their constraints.
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Compare the cheapest LLM APIs in 2026 by price per million tokens, and learn how tokenization and caching affect your real inference costs.
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Compare vLLM, TGI, and Ollama for production LLM inference: throughput, KV-cache efficiency, deployment complexity, and when each engine fits.
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Compare H100, A100 and RTX 4090 cloud rental prices for LLM hosting. Learn VRAM, throughput and cost-per-token tradeoffs to pick the right GPU.
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Compare self-hosted LLM costs against API pricing. Learn the break-even math for GPU hardware, utilization, and tokens to pick the cheaper option.
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Compare GGUF, GPTQ, and AWQ quantization formats on hardware fit, speed, and quality so you can choose the right one for local and served inference.
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Compare Ollama, LM Studio and llama.cpp on setup, performance, control and use cases so you pick the right local LLM runtime for your workflow.
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