Uploaded June 2025 | Updated September 2026, 2 weeks ago
AI is quickly becoming a core part of modern applications, but running large language models (LLMs) locally can still be a pain. Between picking the right model, navigating hardware quirks, and optimizing for performance, it’s easy to get stuck before you even start building. At the same time, more and more developers want the flexibility to run LLMs locally for development, testing, or even offline use cases.
hub.docker.com/catalogs/models?utm_campaign=2025-06-04-docker-llm-runner-krish-naik&utm_medium=video&utm_source=youtube
AI is quickly becoming a core part of modern applications, but running large language models (LLMs) locally can still be a pain. Between picking the right model, navigating hardware quirks, and optimizing for performance, it’s easy to get stuck before you even start building. At the same time, more and more developers want the flexibility to run LLMs locally for development, testing, or even offline use cases.
hub.docker.com/catalogs/models?utm_campaign=2025-06-04-docker-llm-runner-krish-naik&utm_medium=video&utm_source=youtube










