The KV cache is the single biggest lever for LLM inference cost and throughput. How it works, why naive allocation wastes 60–80% of GPU memory, how PagedAttention and continuous batching fix that, what prefix caching actually saves, how FP8/INT8 quantization and GQA/MLA shrink the cache, and why long context is fundamentally a memory-bandwidth problem.
Quantization
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KV Cache Engineering: PagedAttention, Continuous Batching, Attention Variants, and the Bandwidth Wall -
LLM Quantization Compared: GGUF, AWQ, GPTQ, FP8, and the int4 Cliff A practical comparison of the quantization formats that decide whether a model fits your GPU: weight-only versus activation quantization, what calibration data actually buys you, the accuracy cost of int4, and which of GGUF, GPTQ, AWQ, and FP8 makes sense for which workload.
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Quantization Deep Dive: GPTQ, AWQ, GGUF, AQLM, and MLX A rigorous look at every major LLM quantization format: how GPTQ's Hessian-guided rounding works, why AWQ's activation-aware scaling beats naive per-channel approaches, when GGUF k-quants are the right choice, where AQLM wins at sub-3-bit, and how MLX handles Apple Silicon. Includes conversion workflows, a perplexity shootout, and a decision guide.
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Llama 4 and Gemma 4: The 2026 Self-Hosted Model Landscape Meta shipped Llama 4 Scout and Maverick and Google shipped four Gemma 4 variants under Apache 2.0 — all within a few weeks of each other. This is a practical walkthrough of what each model is, what hardware you actually need, how to deploy them with Ollama, vLLM, and llama.cpp, and which one to pick for which job.
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Google's TurboQuant: What It Means for Home AI Enthusiasts Google Research's TurboQuant compresses LLM KV cache memory by 4–5x with minimal accuracy loss. Two weeks in: real benchmarks, active framework integrations, controversies, and what you can actually run today.
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Local LLM Deep Dive: Ollama, Quantization, and Running AI on Your Own Hardware A comprehensive technical guide to running large language models on your own hardware — covering Ollama setup, quantization formats, hardware selection, Apple Silicon and NVIDIA GPU configuration, and building a private local coding assistant.