Rick Beato: Why Local LLMs Beat Cloud Services

What It Is

Music producer and YouTube educator Rick Beato recently demonstrated running large language models entirely on local hardware, making a case for why this approach might surpass cloud-based AI services for many users. His setup uses LM Studio, a desktop application that lets anyone download and run sophisticated AI models like Qwen 3.5 35B without internet connectivity or cloud subscriptions.

The significance here isn’t just the technical achievement - it’s that someone outside the AI development community found local models compelling enough to recommend them publicly. Beato’s background in music production rather than software engineering highlights how accessible this technology has become. The models run completely offline, processing every query on the user’s own machine rather than sending data to remote servers.

Why It Matters

This shift toward local AI represents a fundamental change in how developers and professionals might interact with language models. Cloud services like ChatGPT and Claude offer convenience, but they come with recurring costs, privacy concerns, and dependency on internet connectivity. When a music producer publicly advocates for local models, it signals that the barrier to entry has dropped significantly.

Privacy-conscious professionals now have a viable alternative. Medical researchers, lawyers, writers, and anyone handling sensitive information can process data through AI without that information ever leaving their computer. Financial teams can analyze proprietary documents, developers can review code, and content creators can brainstorm ideas without worrying about data retention policies or third-party access.

The economic angle matters too. Cloud API costs accumulate quickly for heavy users, while local models require only the upfront hardware investment. A machine capable of running 35-billion-parameter models might cost more initially, but it eliminates monthly subscription fees entirely. For small teams or individual professionals, this math often works out favorably over time.

Getting Started

Setting up a local LLM through LM Studio requires minimal technical knowledge. First, download the application from https://lmstudio.ai - it’s available for Windows, Mac, and Linux. The interface includes a built-in model browser that connects to Hugging Face’s repository.

After installation, search for models within the app. Qwen 3.5 35B represents a good starting point, offering strong performance across various tasks. The download size will be substantial (typically 20-40GB depending on quantization), so a stable connection helps for the initial setup.

Once downloaded, loading a model takes just a few clicks. The interface resembles familiar chat applications, making the transition straightforward for anyone who’s used ChatGPT or similar services. For developers wanting programmatic access, LM Studio can expose a local API endpoint:

response = openai.ChatCompletion.create(
 model="local-model",
 messages=[{"role": "user", "content": "Explain quantum computing"}]
)

Hardware requirements vary by model size. A modern GPU with 16GB+ VRAM handles 35B models comfortably, though quantized versions can run on less powerful systems.

Context

Local models aren’t universally superior to cloud services. The largest, most capable models like GPT-4 or Claude 3.5 Sonnet still outperform what most consumer hardware can run. Cloud services also handle scaling effortlessly - teams don’t need to provision hardware or manage infrastructure.

Alternatives to LM Studio include Ollama (https://ollama.ai), which emphasizes command-line simplicity, and text-generation-webui for more advanced customization. Each tool targets slightly different use cases, from developer-focused workflows to user-friendly interfaces.

The real limitation is hardware. Running large models locally demands significant RAM and GPU memory. Smaller models like 7B or 13B parameters work on modest systems but sacrifice some capability. This creates a practical ceiling that cloud services don’t face.

Still, the trajectory is clear. Models keep getting more efficient, and consumer hardware keeps improving. What required a server rack two years ago now runs on a gaming PC. Beato’s endorsement suggests this technology has crossed into mainstream viability, at least for users who value privacy and ownership over absolute cutting-edge performance.