Quick Start: Running TinyLM Offline On-Device
Get a TinyLM model running locally with no network connection and understand what to expect.
What TinyLM Is For
TinyLM packages small language models that run directly on a device — phone, laptop, edge box — with no cloud round trip. This tutorial gets one running and sets honest expectations about what a small offline model can and can't do.
Step 1: Choose a Model Size
Smaller models load faster and use less memory but are less capable; larger ones do more but need more RAM and run slower on modest hardware. The size guide at https://doc.sprapp.com maps model sizes to typical device classes. Pick the largest model your target device comfortably fits.
Step 2: Load It Locally
Initialize the model on-device following the platform setup in the docs. The first load may be slow as weights are read into memory. After that, inference happens entirely locally — you can put the device in airplane mode and it still works.
Step 3: Run a Prompt
Send a short prompt and watch it generate. On constrained hardware, expect slower token rates than you'd get from a hosted frontier model. That's the tradeoff: you gain privacy and offline capability, you give up some speed and raw capability.
Step 4: Test Offline
Disconnect the network entirely and run again. This is the whole point of TinyLM — the model keeps working with zero data leaving the device. For privacy-sensitive or intermittently-connected use, this is the feature that matters most.
Step 5: Set Realistic Expectations
A small on-device model is excellent for focused tasks: classification, extraction, short rewrites, simple Q&A over local context. It is not a drop-in replacement for a large cloud model on open-ended reasoning. Scoping the task to what a small model does well is the key to a good result.
When Offline Beats Cloud
TinyLM wins when data can't leave the device, when connectivity is unreliable, or when per-call cloud cost and latency are unacceptable. It loses when you need maximum capability and have a reliable network — there a hosted model is stronger.
Next Steps
Once a model runs, look at quantization and caching options in the docs at https://doc.sprapp.com to trade a little quality for meaningfully better speed and memory use on your target hardware.