Context Window

Posts that use this term

• Troubleshooting local LLMs (and how to keep up after this series)

  The full catalog of local-LLM failures: OOM, slow tok/s, garbage output, instruction drift, bad RAG hits, tool-call hallucination. Plus where to follow the field once you're on your own.

• Local RAG and embeddings

  Build a working local RAG pipeline in about 30 lines using nomic-embed-text, Chroma, and Llama 3.2. And why running it on your own machine beats the cloud for personal notes.

• Every machine can run a local LLM (here's what fits)

  A per-tier guide to running local LLMs in 2026, from 8GB integrated graphics to a 192GB Mac Studio. Specific models, specific speeds, specific configs.

• Picking a local model by task

  The 2026 open leaders, sorted by what you actually want to do: coding, chat, the small-model crowd, structured output, vision, embeddings, and audio.

• Streaming, throughput, and the KV cache

  Why TTFT and tok/s are different numbers, why streaming feels faster than it is, and the KV cache that makes the 1000th token cost about the same as the first.

• Quantization, distillation, pruning: how a 140GB model fits on your laptop

  Three ways to shrink an LLM, and why one of them does almost all the work. What Q4_K_M actually means and what each shortcut costs you.

• The local-LLM vocabulary

  Parameters, B, dense vs MoE, base vs instruct, tokens, context windows, chat templates, GGUF, and quant suffixes. Read it once and any HuggingFace model card stops being scary.

• The pitch for local LLMs in 2026

  The case for running an LLM on the machine you already own. Privacy, no per-call cost, faster first token, no rate limits, and it works on a flight.

• What leaves your machine when you use AI

  What providers actually see, log, and keep when you call an LLM API in 2026. What "we don't train on your data" really means, how free and paid tiers differ, and when local is the only safe choice.

• LLM API bills, and why a token costs what it costs

  How input and output tokens get priced, why output runs 5-6x more, and how prompt caching cuts the input bill by 10x. Plus the hidden costs that ambush people.

• What it takes to run a model on your own machine

  Why VRAM is the one number that decides whether a local LLM runs, what quantization really does to a model file, and the hardware ladder from an 8GB laptop to a 192GB workstation.

• The major LLMs in 2026

  A field guide to the closed frontier models and the open weights you can actually run. What the "B" numbers mean, and which size fits your machine.

• Where AI actually runs: cloud, local, edge

  When you use AI, a model file is sitting on a real machine. There are only three places it can be, and which one decides almost everything else.

• Prompt, RAG, fine-tune: three ways to shape a model

  Three levers for shaping what an LLM does: prompting (ask better), RAG (give it the right context), fine-tuning (change the weights). What each costs, what each fixes, and how to pick.

• RAG: giving a model memory it doesn't have

  RAG is the pattern of fetching relevant text from a search system and putting it in the LLM's context window before asking your question. Not magic, not fine-tuning, just better prompts.

• The context window, and why models hallucinate

  An LLM only sees a fixed-size slice of text at a time. When it doesn't know something, it predicts anyway. That's a hallucination, not a bug.

• From models to LLMs

  An LLM is one kind of ML model, trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.