LM Studio

Related terms

• Ollama
• llama.cpp
• Inference

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 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.

• Wiring a local LLM into the tools you already use

  How to point VS Code (Continue, Cline), web chat UIs (Open WebUI, LibreChat, Page Assist), and your own code at a local model using the OpenAI-compatible API. Swap cloud for local without rewriting anything.

• 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.

• 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 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.

• 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.

• Install the OpenAI SDK

  Install the OpenAI SDK for Python and Node, set your API key, and prove it works with a one-line chat call.

• 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.