Short answer
Start with the computer you already own. A local AI app can be enough for learning, occasional writing, and small experiments. LM Studio offers a desktop model picker, chat interface, local server, and documented offline operation after download. Ollama offers a command-line workflow and local API suitable for developers. Neither app requires a PebbleRack machine. If your task works well enough on existing hardware, a new purchase may not be useful. PebbleRack has not tested a shipping device, software image, or performance claim. This guide is a source-verified comparison, not a benchmark.
Best for
Choose a desktop app if you want to browse compatible models and talk to them without building an integration. LM Studio's documentation describes locally downloaded models, a local server, and optional cloud/remote choices. Choose Ollama if you prefer terminal commands and a documented HTTP API to integrate with your own applications. The model is separate from the runtime: its license, size, context, tool support, and format determine whether a particular task is feasible. A runtime installation does not supply a model that automatically understands your files or can safely administer your lab.
For a first experiment, use one small licensed model and one real task. Record the host OS, memory, model name and quantization, input size, response time, and whether the output was actually correct. Compare that with a hosted assistant you already use. The same prompt across two tools may be misleading if one has search, files, tools, or a stronger model enabled; note those differences.
Why use local
After the app and model are downloaded, some core inference workflows can run without an internet connection. LM Studio documents that its downloaded-model chat and document chat can operate offline; its model discovery, downloads, runtime downloads, and update checks use connectivity. Local inference lets you choose when to update and may help with repetitive, eligible tasks where direct control matters. It can also offer a convenient local API for tinkering with a home lab. That API must be protected; LM Studio warns that binding beyond localhost exposes it to other devices and recommends authentication.
Local apps can coexist with cloud models. LM Studio's current model picker distinguishes local, cloud, and remote models, and Ollama has local and cloud offerings. Read the selected model and account state before assuming where a prompt runs. A locally installed UI can send a request to a remote model, and a remote client can reach your own home server. The data path belongs to each workflow, not the app icon.
When not to use local
A cloud assistant can be easier for occasional questions, web-connected research, and tasks needing its integrated tools. A direct API can be cheaper than buying hardware for sporadic automation, depending on usage and provider terms. A rented GPU can let you try a larger model temporarily. Local software may be slow or unable to load a model on your current computer, and larger context or multiple users increases resource needs. If you do not want to maintain updates, storage, remote access, and backups, a managed option may be preferable. Do not buy a 128GB system just because a small model ran locally once; the relevant model and task must be measured on an exact configuration.
Decision checklist
- What task must succeed, and how will you judge its answer or output?
- Does a small model fit and perform acceptably on your current machine?
- Is the selected model local, cloud, or on another machine, and what data crosses the network?
- What files, logs, model downloads, and update checks occur?
- Does the model license allow your intended use?
- Can you keep a local API private and recover from a failed update?
- Is setup and maintenance time worth the control for this task?
If any answer is unknown, test that item before buying a new computer. A capacity label such as 128GB unified memory does not tell you model quality, usable accelerator allocation, throughput, or software compatibility.
One practical next step
Install one of the official apps on your existing computer, download a small model, disconnect from the network after the download, and repeat one non-sensitive task. Record success, latency, and what stopped working offline. Then compare a hosted tool on the same task with its network features stated. If you want to follow PebbleRack's future tested setup diaries, you can opt into updates; these learning steps are free to use without signing up.
Sources
Official sources checked 2026-09-29: LM Studio offline operation; LM Studio model choices; LM Studio local server exposure; Ollama Linux instructions; Ollama API documentation.