Local AI Is the New Cold Wallet: 8GB VRAM and Zero $20 Monthly Cloud Bills

Open-weight AI models now trail the best closed models by only about four months on average, while more of them can run on hardware consumers already own. Unlike closed systems, open-weight releases make their trained weights—the files containing everything the model learned—available for download and local execution, with Ollama and LM Studio providing free interfaces for running models and customizing them.

Local operation can reduce recurring cloud costs and keep conversations on the user's computer. LM Studio's documentation says the app "can operate entirely offline, just make sure to get some model files first," and notes that documents attached to a chat can also be used entirely offline. By comparison, a cloud chatbot transmits prompts to a company's servers; OpenAI was court-ordered to preserve ChatGPT logs, including deleted ones, as Decrypt reported in a review of privacy-focused AI tools. Paid chatbot subscriptions generally cost about $20 per month, while Ollama and LM Studio are free.

Hardware requirements depend heavily on model size. Models are measured in parameters, and larger versions require more storage and memory, including a graphics card's VRAM or a Mac's unified memory shared by the processor and graphics chip. Gemma 4 downloads available through Ollama range from about 4.6GB to 20GB. As a general baseline, at least 8GB of VRAM can accommodate smaller compressed models, while more demanding setups may use a gaming GPU with 24GB of VRAM or more, multiple GPUs, or AI systems with unified memory such as the Mac Mini or Nvidia DGX Spark.

Licenses determine how downloaded weights can be used. DeepSeek's R1 series is available under the MIT License, and its model card says the models "support commercial use, allow for any modifications and derivative works." Google's Gemma 4 and OpenAI's gpt-oss use Apache 2.0. Models released under MIT or Apache 2.0 can be used, modified and adapted, including for specialized tasks. Quantization can further reduce storage requirements by storing weights with fewer bits; Hugging Face's documentation describes representing weights and activations "with" fewer bits, allowing larger models to run within more limited memory configurations.