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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.

• Fine-tuning a model locally

  When fine-tuning is actually the right call (it usually isn't) and how to pull off a LoRA run on a 16GB Mac, with a worked Llama 3.2 3B example.

• Local agents and tool use

  Function calling on open models in 2026. Which ones actually work, why local agents break when they break, and the scaffolding that keeps them upright.

• 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.

• 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.

• The runtimes: llama.cpp, Ollama, LM Studio

  llama.cpp is the engine. Ollama and LM Studio wrap it. What each one does, when to reach for which, and why the OpenAI-compatible APIs are mostly but not entirely interchangeable.

• Why Apple Silicon punches above its weight on local LLMs

  Unified memory lets the GPU see all of RAM. Here's why that beats a discrete-GPU PC past 32B parameters, what fits in 16/32/64/128/192GB, and where Apple Silicon still loses.

• 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.

• How a model learns: training and inference

  Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.

• What makes a model: data and algorithm

  A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.

• Install LM Studio

  Install LM Studio on macOS, Linux, and Windows, then flip on the local OpenAI-compatible server so any client library can talk to a model on your own machine.

• Install llama.cpp

  Build llama.cpp from source with Metal or CUDA, then run a GGUF model with llama-cli. The closest thing to bare-metal local inference.

• Install Ollama

  Get Ollama running on macOS, Linux, or Windows, pull your first model, and confirm it works with ollama list. The shortest path to a local LLM.