KV-Cache, PagedAttention, and the Building Blocks Every LLM Inference Pipeline Needs
KV-cache, PagedAttention, and continuous batching form the inference pipeline core. Learn how memory management determines your LLM latency and cost.
Inference
Inference is the process of running a trained machine learning model to generate predictions, classifications, or text in real time.
For large language models, inference involves autoregressive token generation, memory management through KV-cache, and careful balancing of latency against throughput to meet production requirements. Also known as: Model Inference, LLM Inference.
What this topic covers
- Foundations — Inference is where training meets reality, converting static model weights into dynamic output one token at a time.
- Implementation — Deploying inference at scale means choosing the right serving framework, configuring batching strategies, and managing GPU memory under load.
- What's changing — Inference costs dominate production AI budgets, and the hardware landscape is shifting fast.
- Risks & limits — Running inference at scale raises questions about energy consumption, equitable access, and the hidden costs of always-available AI.
This topic is curated by our AI council — see how it works.
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Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Memory Walls, Quadratic Context Costs, and the Hard Engineering Limits of LLM Inference in 2026
LLM inference hits hard physical walls — memory, quadratic attention, bandwidth. Learn the engineering limits and 2026 workarounds shaping real-world AI costs.
What Is Model Inference and How LLMs Generate Text Through Autoregressive Decoding
Model inference generates LLM text one token at a time via autoregressive decoding. Learn why this sequential bottleneck shapes every optimization in modern AI serving.
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Build with Inference
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
Inference Optimization for Developers: What Transfers and What Breaks
LLM inference breaks your cost model, scaling instincts, and test expectations. Learn what transfers from backend engineering and what fails silently.
How to Deploy and Optimize LLM Inference with vLLM, TensorRT-LLM, and SGLang in 2026
Deploy production LLM inference with vLLM, TensorRT-LLM, or SGLang. Covers workload profiling, engine selection, FP8 quantization, and load testing.
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What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated March 2026
Cerebras vs. Groq vs. GPU Clouds: The Custom Silicon Bet Reshaping Inference Economics in 2026
Cerebras, Groq, and SambaNova challenge GPU dominance in LLM inference. The 2026 custom silicon race, real cost shifts, and what it means for your stack.
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Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Always-On AI: The Environmental Price and Access Inequality of Large-Scale Inference
AI inference runs 24/7 on energy, water, and carbon. The environmental cost is real, the access gap is widening, and accountability remains an open question.