About LLM Academy
Practical, benchmark-driven guides for engineers putting LLMs into production.
Our Mission
LLM Academy exists to help engineers put large language models into production — reliably, cost-efficiently, and observably. We publish practical, benchmark-driven guides for the people shipping LLM applications: which inference engine to pick, how to cut API spend 70-85%, how to trace and evaluate every call.
Every article is grounded in published 2026 benchmarks and the official documentation of the tools we cover. We cite our sources, date-stamp our versions, and tell you when to verify on your own hardware.
What We Offer
Four content clusters covering the full LLM production stack — from choosing an inference engine to cutting API costs to monitoring quality in production:
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Inference Engines
vLLM, SGLang, TGI — benchmarks, deployment, and engine selection
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Gateways & Cost
LiteLLM, Portkey, OpenRouter — routing, caching, and cost optimization
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Observability & Evals
LangSmith, Langfuse, Phoenix — tracing, LLM-as-a-judge, monitoring
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Inference Optimization
Flash Attention, KV cache quantization, prefix caching, speculative decoding
Who Is This For?
- ML/AI Engineers — Deploying and serving LLMs in production, choosing inference engines and tuning performance
- Backend Developers — Integrating LLM APIs and optimizing cost through gateways, routing, and caching
- Platform / DevOps Teams — Operating LLM infrastructure: tracing, evaluation, cost monitoring, and reliability
- Engineering Leaders — Evaluating tools, benchmarking options, and managing LLM spend across teams
Technology
Our calculators and deep-dive visualizations are built with a minimalist philosophy — no frameworks, no build tools. Just HTML, CSS, JavaScript, and Three.js for the interactive diagrams that illustrate attention and cache mechanics.
Three.js
WebGL
Vanilla JS
CSS Custom Properties
Zero Dependencies
The entire site is static HTML — no server, no database, no tracking beyond basic analytics. Fast, accessible, and available to anyone with a modern browser.
Academic & Technical References
Our articles are grounded in the foundational papers and engineering documentation of the LLM production stack. Each cluster cites the original sources:
- Efficient Memory Management for Large Language Model Serving with PagedAttention — Kwon et al. (2023). The vLLM paged KV-cache innovation.
- SGLang: Efficient Execution of Structured Language Model Programs (RadixAttention) — Zheng et al. (2023). arXiv:2312.07104. The prefix-tree cache.
- FlashAttention: Fast and Memory-Efficient Exact Attention — Dao et al. (2022). arXiv:2207.08608. Tiled attention.
- FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision — Shah et al. (2024). arXiv:2407.08608. Hopper-optimized attention (740 TFLOPs/s).
- DeepSeek-V2: Multi-Head Latent Attention (MLA) — DeepSeek-AI (2024). KV cache compression via low-rank projection.
- Fast Inference from Transformers via Speculative Decoding — Leviathan et al. (2023). Draft-verify decoding.
- Evaluating Scoring Bias in LLM-as-a-Judge — arXiv:2506.22316 (2025). Judge reliability and human correlation.
Project
Created with ❤️ for the AI engineering community. LLM Academy publishes practical, benchmark-driven guides for engineers putting LLMs into production — inference engines, gateways, observability, and optimization.
Our content is developed against published 2026 benchmarks and the official documentation of the tools we cover. Every article date-stamps its versions, cites its sources, and tells you when to verify on your own hardware.
Have feedback, found an error, or want to contribute? We'd love to hear from you!
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