GPU

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.

• Your first local LLM, start to finish

  Install Ollama, pull Llama 3.2 3B, chat with it, hit its API, and fix the five things that break on a first install. You finish with a working local LLM.

• 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.

• System requirements by OS for local LLMs

  What macOS, Linux, and Windows each need before you run a local LLM in 2026. Mac is the smoothest, Linux gives you the most knobs, and native Windows finally just works.

• 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.

• 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.

• 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.

• 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.

• 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.

• Inside AI: machine learning and deep learning

  Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks, and that's where today's models live.

• 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.