Cost-and-Compute: developers are budgeting LLM experiments by juggling local speed and cloud scale. The punchline: LiteLLM on embedded Linux is becoming a viable tinkering path, while the cloud still handles the heavy lifting [2].
Cost realities - Vast, Lambda Labs, Jarvislabs offer cheaper hourly GPU options than AWS/GCP. You pay for time used, not idle downtime, which helps early-stage work [1]. - Colab can squeeze in a couple of free hours for quick tests, so beginners often prototype locally before scaling [1]. - Hosted routes like OpenRouter give pay-per-inference options; many teams start in the cloud and switch to local or rented GPUs as needed [1].
Local-first model picks - cogito-preview-llama-3B-4bit — a solid fit for ~16GB RAM laptops, good for doc-QA workflows [3]. - granite-3.3-2B-Instruct-4bit — clean candidate lists with clear outputs, though the reasoning depth can vary [3]. - Llama-3.2-3B-Instruct-4bit — often praised for speed but may miss citation details [3].
Phone translation on-device - Pocket Pal — compact enough to run on many phones, with ~6GB RAM needed for 3-4B models [4]. - Gemma — a go-to for translation, including offline mode in some setups [4]. - Seed X Instruct 7B and Seed X PPO — lightweight translation LLMs that could be packaged for mobile use [4]. - ChatterUI and similar local-chat apps show how you can string these models into an everyday interface [4].
Closing thought: your budget and your task decide the path. Start local with LiteLLM, then scale to cloud only when the need for bigger models or throughput arrives [2].
References
[d] how to develop with LLMs without blowing up the bank
Discusses budget-friendly approaches to experimenting with LLMs, local vs cloud compute, pay-per-use, and service options for development and learning purposes.
View sourceHow to Deploy Lightweight Language Models on Embedded Linux with LiteLLM
Guide to deploying lightweight language models on embedded Linux with LiteLLM
View sourceA 5-minute, no-BS way to pick a local model for your real task
Discusses local LLMs for doc-QA on 16GB RAM, favors cogito-3B-4bit; Granite fair; Llama bad (no citations) in test scenarios overall.
View sourceReplacing Google Translate with LLM translation app on smartphone?
User explores LLM translation on phone; compares OpenRouter, Seed X Instruct 7B, Gemma; notes lack of LLM apps; alternatives discussed.
View source